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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 94-775 Lecture 3 Jupyter notebook\n",
"\n",
"Author: George Chen (georgechen [atsymbol] cmu.edu)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Often times, at the top of the notebook, you place boilerplate code that only needs to run once. These few lines you've seen in the previous lecture's notebook. Following the cooking analogy for data analysis, this boilerplate code is like setting up the kitchen."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we want to be able to plot and also work with datetime objects\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"\n",
"# the lines below are just for aesthetics\n",
"plt.style.use('ggplot') # if you want your plots to look like ggplot (like how R makes plots)\n",
"%config InlineBackend.figure_format = 'retina' # if you use a Mac with Retina display"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We continue off our running tomato price example. Today, we look at three problems.\n",
"\n",
"1. As a warm-up, we look at two ways to plot tomato price data specifically for the year 2015. This is relatively straightforward.\n",
"2. As a more complicated example, we look at how to plot the tomato price data so that now each month of each year has exactly 1 plotted data point that has y-coordinate given by the average modal tomato price for that month (across modal prices recorded for that month in that specific year). For example, April 2012 would have a single point. To solve this problem, we use a Python dictionary.\n",
"3. We finally look at how to plot tomato price data so that each calendar month has a single data point with y-coordinate given by the average modal tomato price for that month *across all years*. So April would have a single point. To solve this problem, we can actually just work with lists. However, the actual plotting is a bit different from what we've seen previously.\n",
"\n",
"A key recurring idea in code for data analysis is keeping track of indices and dimensions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Plotting prices only in 2015\n",
"\n",
"Here, we look at how to \"filter\" prices so that we only plot some of the pricing information. The simplest way to do this is to simply ignore data that don't meet our search criteria (such as being in year 2015) when loading in the data. Another way to do this is to load in all the data and then filter afterward (this can be advantageous if you actually want to apply multiple filters and analyze different parts of the data, so loading in all the data can be helpful; on the flip side, if your dataset is enormous, loading in all the data could be prohibitively expensive).\n",
"\n",
"The first approach that filters as we load in the CSV file is fairly straight forward and just adds an `if` condition before appending data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Tomato Prices Over Time')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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d73xHCxYsGOzrwIEDuuOO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"text/plain": [
"<matplotlib.figure.Figure at 0x10a53bf60>"
]
},
"metadata": {
"image/png": {
"height": 335,
"width": 622
}
},
"output_type": "display_data"
}
],
"source": [
"# opens the file `odisha-tomato-cuttack-banki.csv` (which needs to be in the same directory as this notebook)\n",
"# in read-only mode (specified using the string 'r')\n",
"f = open('odisha-tomato-cuttack-banki.csv', 'r')\n",
"\n",
"# Python has a package `csv` that helps us work with csv files.\n",
"import csv\n",
"reader = csv.reader(f)\n",
"\n",
"line_number = 0 # keep track of which line number we are at, starting from 0\n",
"modal_prices = [] # we build up a list of modal prices, starting from an empty list\n",
"timestamps = [] # we similarly build up a list of datetime objects, starting from an empty list\n",
"for line in reader: # go through each line of the csv file\n",
" if line_number >= 2: # note that we ignore the first two lines because they correspond to headers\n",
" date = datetime.strptime(line[-1], '%d-%b-%y')\n",
" price = float(line[-2])\n",
" if date.year == 2015:\n",
" timestamps.append(date)\n",
" modal_prices.append(price)\n",
" line_number = line_number + 1\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"plt.plot_date(timestamps, modal_prices, 'o')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('INR/100 kg')\n",
"plt.title('Tomato Prices Over Time')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second approach loads in all the data and then filters afterward. I will do this using the `zip` function presented last lecture (remember from Lecture 2 that there are alternatives to using `zip`--think about how you would code the following without `zip`):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Tomato Prices Over Time')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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d73xHCxYsGOzrwIEDuuOO\nOxQMBvXjH/9YpaWlkqRjx47pnnvu0f79+/WjH/1IoVBosM3u3bv1gx/8QGVlZXrooYdUVFQ02Nf3\nvvc99fb26tFHHx3sKxEU3YDEkJfwC3IVfkK+wo/IW/gFuQo/8WO+elV08/0z3aqrqzVv3rwRt4RO\nnDhRS5culSS9++67g59v2bJFR44c0aWXXjpYcJOk0047TfX19ZL6r54b6rXXXtPx48e1fPnyYUWy\noqIiXXfddVHb/OEPf5AkXX/99YMFN0kqLS3VlVdeqePHj2vTpk1uVxsAAAAAAAAZzPdFt7Hk5/c/\nsm5oQa65uf9ZDBdccMGI5SsrKxUMBgef8+akzezZsyVJu3btGva5kzYDywAAAAAAACC7+P5FCqP5\n9NNP9ec//1nS8MLXBx98ICn6pYJ5eXkqLS3V3r17tX//fk2fPl3SZ7dvTp06dUSbSZMmKRgM6uDB\ng+rt7VUwGFRPT48OHTqk8ePHa9KkSSPaTJkyZVgssdx9991RP3/44YclfXapph8MFEL9FDOyH3kJ\nvyBX4SfkK/yIvIVfkKvwk1zO16y90u3pp5/W3r17NXv27GFFt3C4/01+hYWFUdsNfD6wnJs2Tpf/\n5JNPnK0MAAAAAAAAfCUrr3T7/e9/r4aGBk2bNk233357XG0Tea/E0Dekern8wBVto/HTwwj9+ABF\nZD/yEn5BrsJPyFf4EXkLvyBX4Sd+zFdepDCKV155Rb/61a80ffp03XfffcNeYiBFv5JtqO7u7mHL\nOWkz8HlBQUFcy492JRwAAAAAAAD8LauKbi+99JKefPJJnX322brvvvs0ceLEEcsMPJdt4DltQ336\n6ac6cOCA8vLyVFZWNvj5QIUz2jPYDh8+rN7eXhUXFysYDEqSxo8fr8mTJ6unp0eHDx8e0ebDDz8c\nFgsAAAAAAACyS9YU3V588UU99dRTOuecc3TffffpzDPPjLpcdXW1JGnnzp0jftbS0qLe3l6FQiGN\nGzfOUZsdO3ZIkqqqqhyPM9BmYBkAAAAAAABkl6x4pttzzz2nZ599VjNmzND3v//9EbeUDjV//nw9\n/fTTeuONN3TVVVfpvPPOkyT19fVpzZo1kqRly5YNa7NkyRKtW7dOr7zyihYvXqzS0lJJ0rFjx/TC\nCy9EbbN06VK9/vrrev7553XhhRcOxnTgwAFt2LBB48aN0+LFiz1ZfwCZx3a0y7Y2Sd1h2b4eGWuk\nYFAqKJSZWSMzrWJwGbu/U/r4kMyZxbLjg2Muq+7wsM+9jHO08YbGP/Bv29sdNeZoyyaznZOYB5aR\nNOY6pSPmWDkztA+VTdWJS/5Z+RUzYu6fscYBAAAAvJas8xW/MzaRNwdkgE2bNumJJ55QIBDQ8uXL\noz4nrbS0dFiB66233tLPfvYzjRs3TgsWLFBRUZG2bdumzs5OzZ8/X3fccceIlxy8/PLL+uUvf6kJ\nEybokksuUX5+vrZu3aqDBw+qtrZWK1asGDHuqlWr1NDQoOLiYl188cU6ceKE3nzzTR09elS33HKL\nli9f7sk2iHarbKby4wMUkf28zEvb0qRIwxqpbdfYCxYU9n8hOTHasqEqBWrrZSprvI0zntgyhR9j\nPrn/JDnLmZNM4emy4Tjefp1AngCJ4DsffkTewi/IVWSKMc8rTv4eetaiKyT5K1+9epGC74tuzz77\nrJ577rkxl/niF7+o+++/f9hnra2teuGFF9TW1qa+vj5NmTJFS5Ys0dVXX61AIPpdt9u2bdP69ev1\n3nvvyVqr6dOn68orrxzzirVNmzZpw4YN2rdvn4wxOvfcc3Xttddq7ty58a7qqCi6AYnxKi8jmzfK\nrn5cSuW0aozMipUKLFzquEla4sTojEn+vnCRJ0Ci+M6HH5G38AtyFZnA0XmFMTrj3+9Rwb/U+ipf\nKbphEEU3IDFe5KVtaVLk0XvTU8gyRoE7HnR0JVNa40R6xZEngBf4zocfkbfwC3IV6RbXeYUJaOL9\n/4+Olp+T9Li84lXRLWtepAAA6RRpWJO+Qpa1ijSsdbRoWuNEesWRJwAAAMBY4jqvsBF98uwvkxtQ\nhqLoBgAJsh3tjp/HlTRtzf1xjCEj4kR6OcgTAAAAYCxuziuO79qRk7+HUnQDgATZ1qZ0hyApdhyZ\nEifSizwAAABAItz+PpmLv4dSdAOARGXKWzNjxZEpcSK9yAMAAAAkwu3vkzn4eyhFNwBIVEFhuiPo\nFyuOTIkT6UUeAAAAIBFuf5/Mwd9DKboBQILMzMx4G2SsODIlTqQXeQAAAIBEuP19Mhd/D6XoBgAJ\nMtMqpFBVeoMIVffHMYaMiBPp5SBPAAAAgLG4Oa8YVzU7J38PpegGAB4I1NZLxqRncGMUqK1ztGha\n40R6xZEnAAAAwFjiOq8wAZ3+1X9NbkAZiqIbALhgO9oV+dN6RRrWKvKn9dIZk2Ruui31BS1jZFas\nlKl0dqm2qaxJT5wYXSr2RZx5AgAAAIzF8XmFMTrj37+n4Kx5qQksw+SnOwAA8BPb0qRIwxqpbdfw\nzyUpVCXzlZtl39kmtTWP3VFBofO394y2bKhagdq6uAspgUXLZEvKFGlYGz3OeGLLFH6M+eT+kzT6\nvojCFBbJho/FPQ4FNwAAAHgp5nnFyd9DCxZdkfrgMgRFNwBwKLJ5o+zqxyVroy/Qtkv2f9/tv6Lo\n6/+XbGuT1B2W7euRsUYKBqWCQpmZNTLTKmQ72mVbm2T3d0ofH5I5s1h2fHDMZdUdHva5W6ayRnmV\nNaP2O/TzofEP/Nv2dkeNOdqyyWznJOaBZSSNuU7piHnAqfsiWh8qm6rJl/yz8itm6B9N22Ouixd5\nAgAAAIwl1nlFrjPWjnb2CL/o7OxMdwiOlZSUSJK6urrSHAnwGSd5aVuaFHn03tELbkMZo8AdD3Jl\nETzHHAo/IV/hR+Qt/IJchZ/4MV/Ly8s96YdnugGAA5GGNc4KbpJkbf8l1gAAAACAnEXRDQBisB3t\nI57hFlNbc387AAAAAEBOougGADHY1qaUtgMAAAAA+B9FNwCIxe1bMf32Nk0AAAAAgGcougFALAWF\nqW0HAAAAAPA9im4AEIOZ6e4tpG7bAQAAAAD8j6IbAMRgplVIoar4GoWq+9sBAAAAAHISRTcAcCBQ\nWy8Z42xhYxSorUtuQAAAAACAjJaf7gAA29He/5bH7rBUUCgzsyarrhByu35D29m+HhlrpGAwK7eR\nH5jKGpmbbpNd/bhk7dgLf+GfZDvbpTMmsZ8AAAAAIEdRdEPa2JYmRRrWSG27hn8uSaEqBWrrZSr9\n+0wst+s3ZjuHfSA5AouWyZaUKdKwVmprHn3B1ndkW99hPwEAAABADsu7//777093EEjM0aNH0x2C\nY4WF/W9zPLbhRdn/+r+lgweiL3jwH7JbNkmTSmQqzktdgB6JbN7oav1itnPQB+I3kJfhcDjmsuas\nKQosuKL/VtPdYxTeBrCf4KF4chVIN/IVfkTewi/IVfiJH/N1woQJnvTDM92Qcr3vbHN2i561sqse\nk21pSk1gHrEtTa7Wz3G7MfpAatiWJtn1z2j49YdjNWA/AQAAAECuoeiGlPtk7ZPOC0vW9t/K5yOR\nhjWu1i+udqP0gdRwta/YTwAAAACQUyi6IaVOtO/R8Xd3xteorVm2oz05AXnMdrSPeBZbTG3NiuzY\nGn+7U/rwyzbyO1f7eAD7CQAAAAByBkU3pFTfO9tctbOt/rgtz22cdsuraRsb8Ul0O7OfAAAAACA3\nUHRDSkW6P3HXsNsnD1x0G2fY5XbxYmzEJ9HtzH4CAAAAgJxA0Q0pFSg43V3DgkJvA0kWt3EWutwu\nXoyN+CS6ndlPAAAAAJATKLohpU6bNc9VOzOzxuNIksNtnGb+5WkbG/FJdDuznwAAAAAgN1B0Q0rl\nV8zQuC9eEF+jULXMtIrkBOQxM61CClXF1yhUrcDsi+Nvd0offtlGfudqHw9gPwEAAABAzqDohpQ7\nve4WyRhnCxujQG1dcgPyWKC23tX6xdVulD6QGq72FfsJAAAAAHIKRTekXHDWPJmbbotdtDBGZsVK\nmUp/3Y5nKmtcrZ/jdmP0gdSIe1+xnwAAAAAg5+SnOwDkpsCiZbIlZYo0rJXamkcuEKpWoLbOt0WK\nmOtXXCrzT/Nkzv2CbEe7bGtT/1stCwplblopu+W16O2G8vk28iNX+4r9BAAAAAA5iaIb0sZU1iiv\nsmZkIWNmTVY892ro+kU2/V76f7dJBw/0//DgAdlNv5fd9PvojUNVMitWSn29UndYtq9HxhopGMyq\nbeQXtqVJkYY1UtuukT8csq/s/k7p40MyZxZLZVPZTwAAAACQwyi6Ie3MtIqsLkzYPa3Sn1+WrHXe\nqG2X7P++K7NipQJXXJO84BBTZPNG2dWPj77/huyrvK//W2qDAwAAAABkLJ7pBiSRbWkau2AzZmMr\nu+ox2ZYm7wODI473H/sKAAAAAHAKim5AEkUa1rgruA2wtv+5cEiLuPYf+woAAAAAMARFNyBJbEd7\n9GeAxautub8vpJSr/ce+AgAAAACcRNENSBLb6t2thl72BWfcbnP2FQAAAABAougGJE93ODP7gjNu\ntzn7CgAAAAAgim5A8hQUZmZfcMbtNmdfAQAAAABE0Q1IGjOzJiP7gjNutzn7CgAAAAAgUXQDksZM\nq5BCVYl3FKru7wsp5Wr/sa8AAAAAACdRdAOSKFBbLxnjvgNjFKit8y4gxCWu/ce+AgAAAAAMkZ/u\nAAC/sB3tsq1Nsvs7pY8PyZxZLJVNlZlZM+LqpoFl1R2W5i2Utv1Fsja+AY2RWbFSpjL67YrDxigo\njBoHEmMqa2Ruuk129eNj778Y+woAAAAA/IzzT3cougEx2JYmRRrWSG27hn8+9L+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"text/plain": [
"<matplotlib.figure.Figure at 0x10bdf8da0>"
]
},
"metadata": {
"image/png": {
"height": 335,
"width": 622
}
},
"output_type": "display_data"
}
],
"source": [
"# opens the file `odisha-tomato-cuttack-banki.csv` (which needs to be in the same directory as this notebook)\n",
"# in read-only mode (specified using the string 'r')\n",
"f = open('odisha-tomato-cuttack-banki.csv', 'r')\n",
"\n",
"# Python has a package `csv` that helps us work with csv files.\n",
"import csv\n",
"reader = csv.reader(f)\n",
"\n",
"line_number = 0 # keep track of which line number we are at, starting from 0\n",
"modal_prices = [] # we build up a list of modal prices, starting from an empty list\n",
"timestamps = [] # we similarly build up a list of datetime objects, starting from an empty list\n",
"for line in reader: # go through each line of the csv file\n",
" if line_number >= 2: # note that we ignore the first two lines because they correspond to headers\n",
" date = datetime.strptime(line[-1], '%d-%b-%y')\n",
" price = float(line[-2])\n",
" timestamps.append(date)\n",
" modal_prices.append(price)\n",
" line_number = line_number + 1\n",
"\n",
"price_date_tuples_filtered = [(price, date) for price, date in zip(modal_prices, timestamps) if date.year == 2015]\n",
"prices_filtered = [price for price, date in price_date_tuples_filtered]\n",
"dates_filtered = [date for price, date in price_date_tuples_filtered]\n",
"# note that there's actually a shorthand for the two lines above:\n",
"# prices_filtered, dates_filtered = zip(*price_date_tuples_filtered)\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"plt.plot_date(dates_filtered, prices_filtered, 'o')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('INR/100 kg')\n",
"plt.title('Tomato Prices Over Time')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we instead wanted to plot all the years up and including 2015, we would use `<=` instead of `==`. Similarly, if we wanted all the years after and including 2015, we would use `>=`. There's also `<` for strictly less than, and `>` for strictly greater than. If we wanted to plot all years excluding 2015, we would use `!=`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Plotting an average price per month within each year\n",
"\n",
"We now look at making a plot where for each month in each year, there is exactly 1 point that we plot. To do this, we shall make use of Python dictionaries.\n",
"\n",
"Recall that a list `x` allows us to access elements of `x` via\n",
"`x[0]`, `x[1]`, and so forth up to `x[len(x) - 1]` (or `x[-1]`). If you want, you can think of `x` has a hotel, and its rooms are 0, 1, 2, ..., `len(x) - 1`. Each room stores a value.\n",
"\n",
"A dictionary is kind of like a list except that the hotel rooms don't have to be nonnegative integers 0, 1, ... Instead, they can be any sort of unchangeable value (in Python, values that cannot change are called \"immutable\"). In particular, Python calls what I'm calling room numbers to be \"keys\". Instead of having keys 0, 1, 2, and so forth, now keys can have arbitrary immutable labels. We could for example have keys that are strings, numbers, tuples, etc. For example:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"5\n",
"6\n",
"eggs\n",
"UDAP\n"
]
}
],
"source": [
"x = {'cat': 4, 'dog': 5, 'shark': 6, 7: 'eggs', (9, 4, 7, 7, 5): 'UDAP'}\n",
"print(x['cat'])\n",
"print(x['dog'])\n",
"print(x['shark'])\n",
"print(x[7])\n",
"print(x[(9, 4, 7, 7, 5)])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Since the keys can now be quite complicated, Python in general does not guarantee that the keys are in any particular order. So there isn't a key that is guaranteed to be the very first key (what would have been room 0 in a list), etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check whether a key is in a dictionary as follows:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'cat' in x"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"9 in x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create a new \"hotel\" room as follows (let's say we want the key to be 9):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"onsen\n"
]
}
],
"source": [
"x[9] = 'onsen'\n",
"print(x[9])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's return to the tomato prices in India. If we want to average across prices per month per year, then what we can do is let each month-year pair (this is a tuple with 2 elements) be a hotel room, and inside the hotel room, store a list of all the prices seem for that particular month-year pair! For example, `(4, 2012)` would be the key that corresponds to April 2012, and the value in the dictionary for this key would be a list of prices."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Tomato Prices Over Time')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10bdf81d0>"
]
},
"metadata": {
"image/png": {
"height": 335,
"width": 622
}
},
"output_type": "display_data"
}
],
"source": [
"# opens the file `odisha-tomato-cuttack-banki.csv` (which needs to be in the same directory as this notebook)\n",
"# in read-only mode (specified using the string 'r')\n",
"f = open('odisha-tomato-cuttack-banki.csv', 'r')\n",
"\n",
"# Python has a package `csv` that helps us work with csv files.\n",
"import csv\n",
"reader = csv.reader(f)\n",
"\n",
"line_number = 0 # keep track of which line number we are at, starting from 0\n",
"modal_prices = [] # we build up a list of modal prices, starting from an empty list\n",
"timestamps = [] # we similarly build up a list of datetime objects, starting from an empty list\n",
"\n",
"prices_by_month_year_tuple = {} # the dictionary we will build up\n",
"month_year_tuples = [] # we store this list to get the month-year pairs in chronological order\n",
" # (we assume the CSV file has the rows ordered in chronological order);\n",
" # this is important since a Python dictionary does not have to have its\n",
" # keys in any particular order!\n",
"for line in reader: # go through each line of the csv file\n",
" if line_number >= 2: # note that we ignore the first two lines because they correspond to headers\n",
" date = datetime.strptime(line[-1], '%d-%b-%y')\n",
" price = float(line[-2])\n",
" \n",
" month_year_tuple = (date.month, date.year) # we use this as the key\n",
" \n",
" if month_year_tuple not in prices_by_month_year_tuple:\n",
" # the key is not already in the dictionary `prices_by_month_year_tuple`\n",
" prices_by_month_year_tuple[month_year_tuple] = [price] # list with a single entry `price`\n",
" month_year_tuples.append(month_year_tuple) # keep track of month-year pairs (in chronological order)\n",
" else:\n",
" # the key is already in the dictionary `prices_by_month_year_tuple`\n",
" prices_by_month_year_tuple[month_year_tuple].append(price)\n",
" line_number = line_number + 1\n",
" \n",
"def average(list_of_values):\n",
" return sum(list_of_values) / len(list_of_values)\n",
" \n",
"dates = []\n",
"average_prices = []\n",
"for month, year in month_year_tuples:\n",
" dates.append( datetime(year, month, 1) )\n",
" average_prices.append( average(prices_by_month_year_tuple[(month, year)]) )\n",
" # print((month, year), ':', average(prices_by_month_year_tuple[(month, year)]))\n",
" \n",
"plt.figure(figsize=(10, 5))\n",
"plt.plot_date(dates, average_prices, 'o')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('INR/100 kg')\n",
"plt.title('Tomato Prices Over Time')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Plotting an average price per month, where the average is across years\n",
"\n",
"In this last part, we look at how to plot the average prices so that the x-axis says \"January\", \"Februrary\", ..., \"December\", and the y-axis gives the average modal tomato price per month. Because there are exactly 12 months, and months are ordered, we can actually just use a list rather than a dictionary as we did above. For the actual plotting, however, there's now an extra piece needed to get the labels to display correctly."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Tomato Prices Over Time')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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aUpeBvFdjF29uxZ/uu//gsvLy8WLVpEQEAAdXV15OXlfWDwVlhYSH5+PuHh4YSGhk7xO7mz9PT0\n8NOf/pSMjAzS09Npb29nYGCAyMhITCYTTk5O2NnZkZWVRXBwMLGxsWoDPoEP66gHBATQ09NDUVER\nbW1tPP744zz66KPGz5ubmzlw4ABVVVVs2bKFxMTEySr2HcXPz4/Q0FAuX75sBG/BwcG4uLjg7u5O\nTEwMaWlppKWlce3aNRobG0lPTyc/P5+rV6/S1dWFxWIhJSVl3DqSMnFGt/H/8i//wsWLF4mPj2fF\nihVj+nmRkZEEBgZSVFREfn4+fn5+WCwW3nnnHY4cOcKaNWu0OdEkGD0L2mKx0NPTY8witbOzw9fX\nl4iICEpLS6msrMTd3Z2wsDD12f/AdrwDtLW10dbWRkNDA87OztjZ2WFvb4+TkxMpKSk0NTWRnZ3N\n4OAg0dHRCt5us1t9L4/ujy9evJju7m4qKyuprq5mwYIFeHh4KHj7FEafB11dXVy9epX29nbjDjez\n2YyXlxfR0dFUV1dTVFSEi4vLtAjeFLrJHcO2EOLQ0BD//d//jaOjIw8//LCxQ4+Pjw/z589n6dKl\nLFq0iMuXL3PmzBlOnDjBwMAAXV1duLi4EB8fr0ZuAthmsdXW1vLd736XxMREnnrqKdLS0nB1daW1\ntZXCwkJOnTrFhQsX8PLyIjY2ltOnTxMeHs78+fNn/ecwuvG3/RkUFERCQgKnTp3i6tWrODo6Gsfs\n6C9oW/B28eJFzpw5w/r163FxcZmy9zLd2GYy7NixA3t7exITEwkNDcXf35/g4OAPDd6cnJwoLCwk\nKSlpWu18dCewt7dnyZIlREZGYrFYyM/PJzc3l5KSEgYHB/H29iYgIID09HSuXLlCamoqrq6uU13s\nO4Kts9nW1kZeXh5ZWVlGqGNbt8TPz4/r16/T2tqKp6encVwXFxfzu9/9juLiYr7whS+wceNGQIuV\nfxAfHx/CwsLGBG9hYWFGSODi4kJAQAArV64kLS0NDw8PLl68yPnz54GbsxzWrFmDs7Oz6vg2MZlM\nNDc3893vfhdfX1++8IUvsHnzZuNW0dHfmbbgrby8nMzMTDIyMmhsbOTpp59m8+bNgM6F28nWdp0/\nf57du3fz9ttvc+TIEerq6gCM5R58fX2JioqiqKiI8vJyPDw8CA8Pn/Uz3kYHDQcOHOC1115j//79\nZGRkkJWVRXt7O1FRUbi6uuLk5MSiRYuM4O3GjRtER0fj4uIyq+vwdhn92TQ0NFBfX09JSQk3btzA\n1dUVR0dHABYtWsS1a9coKiqisrKShQsXKnj7hEbX9eHDh9m1axdvvfUWR48epbi4mMHBQWNmure3\nNzExMVRWVlJYWIirqysRERFTetu6Qje5I9hOtKtXr+Ls7MyhQ4dYsWIF9957r/FzwLj4CAoKYtWq\nVSxatAh3d3e6uroYHBzEZDKxcuVKzQiaALadwV555RWsVisrVqwgPDwcLy8vkpOT2bBhgzHykJWV\nxcmTJ2loaKCnp4fm5maWLFkyqxfxHv3l0dTUREtLi7G+UmBgIElJSZw8eZKqqiq8vLyMhbnfH7zF\nxsby8MMPG4vmznaj6ycvL4+qqiq2bt3K0qVLgZujjwEBAR8avNnC+8WLF0/Z+7iTjN412mQy4ezs\nTFRUFEuXLmXp0qWMjIxw4cIFMjMzOXXqFD4+PnR0dNDW1mbMPlTH88PZBjnOnj3Ljh07yMzMpKam\nhpycHKPt8PPzw8vLi6CgIIaHh8nMzCQrK4vjx4+Tl5eHvb09Tz/9NA888AAwtg2S99iOxfcHb46O\njmOCt5GREcxmM56eniQlJZGSkkJqaiqdnZ00NDQYS1rouL49ent7efXVV+nr62Pbtm1GGw83Z9x2\ndXVx5coVYy3fyMhIYmJi8PT0JCwsjEceeYQNGzYAOhdup9EzEl966SXOnTuHt7c3Hh4eVFdXU1hY\nSGdnJ4mJiTg5OREQEEBkZCRFRUVUVVUZs1Rm87p7tjbk7bffZteuXYSHh3P//fdz1113GRu4FBYW\nkpaWhpub27jgrauri7i4OA3MTrDR7ca+ffvYuXMnx44do7S0lNOnT1NcXExCQgJeXl7AzRlv165d\no7i4mKqqKs14+4RsdbR7925+85vf4O3tzdq1a4mPj+fcuXPk5uZy7tw5Fi5ciLOzM76+vkRHR1NV\nVUVpaSkmk4k5c+YYff3JptBN7ggmk4nW1la+/vWvU1paSldXF1u3bjUaq/ffwmFbGNfX15eFCxeS\nlJREf38/ubm5xMfHa62mz2D0Bfb169d59913+cIXvsCKFSuAmxeHVqsVs9lMbGwsq1atIi4uDjc3\nN86fP09/fz8Wi4X4+HjCw8Nn5ZfN6C/qQ4cO8Zvf/IZ3332X+Ph4I4Cwba6Qnp5OZWUlHh4etwze\nPDw8NFNoFNsug1lZWfT392O1WtmyZQvw3kyGWwVvfn5+YzZXsF2ozcbj8+Oy1c2tZmza/u7r60tK\nSgr33HMPLi4u9PX1cezYMa5du8bIyAhdXV0sX758Vl9QfRy2mZsvv/wybm5uPPLII9xzzz3Y29uT\nm5tLY2Mj4eHhBAQE4OPjw9KlS5kzZw7R0dEEBATwyCOPsGnTJiNIVsgw3q2O5w8L3mz1Z3u+u7s7\nISEhJCcnc/r0aQYHB7n77rvVftwmV69e5e233yYlJYVHHnkEgIGBAWpqavjhD3/I/v37OXDgAEND\nQ8ydOxcHBwcCAwNJSUlhyZIlhIeHAzoXbjdb2/W9730Pb29vtm3bxjPPPMPdd9/NnDlzSE9Pp76+\n3thky2QyGZte5OTkUFBQwIoVK4zgYrYqLCzkV7/6FStWrGDr1q2kpaURFRWFl5cXGRkZmM1m7r//\nfmNQwMnJicWLFxu3ND7wwAPGz2Ri2Nr2vXv38tvf/pbExESeeOIJnnzySUwmE8XFxRw5coRFixbh\n7e2NyWQygreioiIKCgpYunSp1lb9BLKzs9m5cydr1qxh27ZtrF69muTkZNzd3cnOzsZqtXLvvffi\n5ORkDJ7FxMSQm5tLU1MT69evN2YfTjaFbnLHGBwcpKCggPb2doaGhkhJSSE4OPiWHSZbQ2jrRNu+\nrLOzs/Hw8CAlJWXM8+TjM5lM1NXV8aMf/YjGxkb6+vr4yle+AjBmYdzRgoKCSE1NZe3atbi5uVFd\nXU17eztr166ddYsXj95cYvfu3bz++uvExsayfft2Fi5cCNx6c4UPC97kPYODg7zyyiukp6fT2NhI\nWFgYK1asGNdOjA7eGhoayMjIwMvLi6ioqDHPUx3fmq0+W1tbeeutt3jzzTd59913uXLlCp6enkab\na7Vasbe3x8HBgYSEBFavXk18fDxWq5UbN25w6dIllixZgre3t47pWxhdJ1VVVcbMzXvuuYeIiAiW\nLVuG1WolOzubhoYGIiMjjQX8w8LCiIuLY/HixcYsZBjbBsl7TCYTZ8+e5dVXXyU1NdXomH/UjLfR\nx+zw8DDu7u60t7eTl5dHWloaPj4+U/J+ZrrW1laOHDlCYGAgK1eupKqqikOHDrFz507MZjPz5s1j\neHiYoqIiAgMDP3CpALU5t4+t/Tp8+DBlZWU8+eST3HXXXcDNGf7Hjx+nqamJ//t//68xU9HWPvn7\n+xMREUFCQoLRZ5+NbP3qd999l6amJp577jmio6MByMnJYdeuXZhMJl5++WV8fX0ZGhoy6tDR0ZHl\ny5ezfv163Q1xm1RWVvI///M/pKWl8dRTT5GcnIyXlxdWq5WioiJMJhOPP/44rq6uxvmwePFiLl26\nxNmzZ9m0aZNCt4/Bdh7s27ePq1ev8uUvf5k5c+YAN8+Dt956C4AXXnjB2EQRbvb1fXx8SExMZOPG\njVN6Hih0kzuCxWLB3d2dtLQ0Kisr6ejooLu7m7vuugs7O7sPvFgbHU6EhYVx5MgRnJycWLNmjTpa\nn8GxY8c4ffo0/f39eHp6sm7dOqOeP6hebduax8bG0tDQQGVl5ay8ILHVT0ZGBq+99hpr165ly5Yt\nzJ8/f9zz3r+5Qk1NDQ4ODsybN0/H7wewt7cnODiY1tZW2tvbcXV1Zfny5Tg6Oo5rJ2zBW0BAAGfO\nnCExMZHY2NgpLP2dYfTtQjt27KChoQE3NzccHR3JzMykqqrK2DUQGDN7yGw2ExwcTEpKCl5eXmRm\nZuLi4sLChQt1TN+CbeOg06dPc+bMGaxWK1u3bgXe2x0tKSkJ4JbB2+ilF0b/Trm1nTt3kp+fz7lz\n51i0aNHHDt5sbGFmWVkZDQ0NbNy40Zg1K5/erfp4bm5ulJSUUF5ezsmTJzl69Ci1tbVs3ryZbdu2\ncf/99xMZGcmpU6dwcnJixYoVWtNqktnq+q233sJqtfLVr34VgHPnzrF3714yMjJ47rnnjNt8+/r6\nuHjxIp6enpjNZkJCQoyAabYOylitVkZGRnj99ddxcXHh6aefZmRkhIKCAn7729/S09PDjh07jB17\nOzs7KSwsJDQ0FLPZjKOjo+6GuI1st/Z++ctfNvqPtjB0eHiY73//+/j5+XHjxg3jLiCAZcuWsX79\neu1y/QkMDAywc+dOoqKiePTRRz/0PGhvb+f8+fP4+fkZG7VMdbipoU6ZtmwbJ8DNjuzIyAiBgYH8\n1V/9FTExMZSUlPBf//Vfxs9tFxfvZ+sEFxcX09vbi7u7O0NDQ7f/DcxgW7Zs4XOf+xwdHR3U1tZS\nVlaG2Wz+0A6RnZ0dw8PDODg4cN9992GxWGhqapq8Qk8jFouF3Nxc3N3d2bRpk7FD5uhjHt47rhcs\nWMDf/d3f0dvbyzvvvENvb+9UFHvas9VfUlIS27ZtIyoqipqaGnbv3g3cup0wm80sWLCAF154wej4\nywezjaCfO3eO73//+/j6+vLcc8/xve99jxdffJHExERaWlr44Q9/SEVFxQe2Cc7OziQmJuLp6Ulp\naSk9PT2T/E7uDDdu3OB3v/sdv/nNb2hoaDBGdm0zCG3H85NPPskTTzzBuXPn+OUvf0lVVRWAds/8\nhP7kT/6E5cuXU1JSwg9+8IMxx2VMTAxPPfUUCxYsYM+ePRw9epSrV6+O+x3l5eUUFxcTFBSkwG2C\n2EL+l19+2XjMxcWFb3/72yxfvpyAgABWr17Nt771LbZu3UpYWBhwM5i2t7c3difVuTA1+vr6jIC6\ntbWVt99+m6ysLJ577jnuu+8+43l79uxh165d9Pf3j/sds3V2rm1nUi8vL2P2zpkzZ9i1axe9vb3s\n2LHDGOAC+NnPfsaJEyd0nTNJGhsbcXR0NHYDz8vL4/XXXzdCIFuodv36dXbt2kVfX5/RV9X3w8dn\nWy/YxcXF6PeUlpZ+4Hnwox/9iMOHDzMyMjJVRR5HM91k2nn/jCnbaL7tgtnd3Z2UlBSqq6spLi7m\nypUrpKWlfegtd+3t7fzud7/j6tWr/Omf/qkaus/AVsfJycnAzanVtbW1xMbG4ufn96GvHT0LoLi4\nmGXLlhkXkbNJd3c3v/rVr0hISOChhx760FmCtsdsmyts3LhRI2N/8P5ZC6P/HhAQQEREBE1NTeTn\n52O1WklKShrXTthGHm0jwZoJ8eFMJhPd3d384he/wGKx8MQTT7Bq1SoA3njjDU6dOsX8+fNpbW2l\npKSEuXPnEhAQMGa2m+1PV1dXzpw5w/Xr1401OGQss9lMQEAA/f39VFdX09fXx4IFC4xNakYfz0lJ\nSdjZ2ZGZmUl5eTmrVq3SwtmfwMjICA4ODqSlpXH+/HmKi4tpbGxk8eLF42a8XblyhWPHjmFnZ0dc\nXNyYNQnPnj3LiRMn+H//7/9p/dgJYrFYeP3118nNzaW2tpa1a9cCN9etWrp0KevWrSM1NdUI20wm\nEy0tLbzzzjtcv36dBx98UN+bU8BisWC1Wjlz5gwVFRW4ubkZG7y8P3CrqqrirbfeIjQ0lLS0NK3z\n+Qe2Oqyrq6OsrIy2tjaOHTtGX18fL774IkFBQcZzjx49SnZ2NmlpaSxcuHDWBpWT6cyZM9TX17Nu\n3Trq6urYuXMnfX1940Kgn//851RWVrJy5Upjt3H5+CwWi7FcQHl5OQMDAxw6dIi+vj5eeumlMefB\n4cOHKSgoYPny5SQkJEybPr1CN5lWbLcttbW1cfz4cQ4dOkRhYSEWi4WQkBBjWq6bmxsLFy6kpqaG\n4qWUs+sAACAASURBVOJiOjs7jeBtZGRk3BeNm5sbHR0dfPGLXyQiImIq3tod6VYBhMlkYmhoCLPZ\nTFJSEiMjIxQWFnLu3DkiIyM/Mnirq6szbjX4whe+MOO/fG4VBPf09HDo0CEcHR1ZuXLluEU9ba/p\n6ekhIyPDuL0iICDAuNie7WxtRXt7u7FT1LVr1+jp6TGml/v7+xMZGUljYyPZ2dmMjIwYuwl+UNA5\nXb6cpwvboMdoTU1N7N+/n9WrV7Np0yYA3nzzTd5++202bdrEV7/6VUwmE6WlpZSWlhIdHT3mVlNb\nHVdUVHDgwAFCQkJYs2bNlO0oNZ28fydYAD8/PwICAujp6aG6uhpHR0ciIyPHrClme35iYiJDQ0PE\nx8ezaNGiqXwr09r7N02wzeC0bcK0dOnSMcHbkiVLxgRvISEhtLS0MH/+fGMWlU1ERAT33XffmIsA\n+WxMJhPz58/n2rVr5OfnU1VVxd133w3c/CzNZvOY9WGrq6vZt28fubm5fPGLX2T58uVTVfRZy3ZO\n2WaoZGRkUFdXR0NDA1/5ylfYuHGjcR7aZr91dHTwuc99blb20z9smRyTyURISAinTp2ivr4eq9XK\nyy+/PKaNKSgoYM+ePbi4uPDMM89M+a10M53t8+rv7ycnJ4empiZyc3Pp7e3lhRdeGDPgkp6ezsmT\nJ1myZAnLli2bdWtZfxIfdh6YzWZ8fHyM3dsB/vmf/3lMuFlYWMjevXvx9PTk6aefnlbXmArdZNoY\nvU7QP//zP5Obm0tXVxetra1kZGTQ29s7JnQYHbwVFRVx+fJlli5dOu4C0XYCj962WT6a7fPo6uqi\nqamJ8vJyOjs7CQoKMm5rss14GxoaIjs7m6amJqKioozg7VahXV9fH6dPn+Yb3/iGMSo9U41esHzX\nrl00Nzczf/58XF1dKSgo4NKlS8yfP39Mx2n0gv+7d+9m//79LFmyBA8Pjyl5D9PR+9uK9PR0qqur\nycvL4+TJk1gsFiIiInB2dsbf35+oqCgaGxvJyckZM+NNPlxmZiaVlZUEBwePmYV2/fp17Ozs+OIX\nvwjcXONx165drF27locffhhfX18CAwPJzMykv7+f/Px85syZM6YT2tPTw5EjR2hubuYv//IvNQvl\nDz5oMX9fX1+CgoK4cuUKmZmZt1zM39YmL1iwwAiCNHPz1kwmE01NTRQUFBAWFoaDg8O44C0tLY2m\npiZKS0vHBW++vr4sXrzYmPH9fpq1ObEsFgsuLi7Ex8dz5coVioqKjODNtvyInZ0dV65cYffu3ezZ\ns4eWlhaefvppHnjgAUDnwu1gq1Nb29PX10dvby+dnZ2MjIwYM21DQkIYHBykoqKCkJAQ7r//fnx8\nfDCZTFRWVrJ3717y8/N5+umnjY0WZgur1Tqmr1hdXW1smtPW1kZoaCgWiwUvLy/8/f05c+YMJpMJ\nT09PfHx86O/vZ//+/fz+97/n+vXrfPOb39QM2wn0YSEQYMzYr6+vZ3h4mH//9383Bn7h5u2mb7/9\nNmazmeeee04D5x/g/edBU1MTzc3NnD9/nu7ubqOPaLu+rKurIzAwkIiICLy9vRkaGuLAgQPs27eP\n7u5u/vZv/3banQcK3WRasJ1ozc3N7NixA19fX5588kn+8i//krVr11JRUUFhYSGDg4OEhISMCd5S\nUlIoLy+nrKyM+Pj4caPL6mR9crZQo6Ghgf/4j/9g7969FBQUkJmZSVFREfPnz8fLy8uo2wULFjA0\nNEROTg7nz58nPDwcPz+/cXVvtVrx8vLi3nvv/cgZcTOB7f0fOnSIN954g4GBAVJTU3FxcWFgYICC\nggKuXr1KYmIiLi4uY2YB5efnc/jwYaKjo7nrrrs0C2gUk8nEhQsXeOmll3Bzc+Ohhx5i06ZNREVF\nUV9fb9yyOHfuXFxcXPDz8yMqKorm5maysrLo7++f1buhfRzp6en85Cc/ob6+Hm9vb4KCgsbM9Jk/\nfz4ODg5cvnyZN954AwcHB7Zv327MUHB0dOTkyZOEhIRw4cIFEhISxuwe6OjoiKOjI5s3b57x4fsn\n9UGL+fv6+n7sXTRt9P03lu0CamBggH/5l38hPT3dqNdbBW/Lli0jPz+furq6cbea2updYc7Eev9F\nrq0/YrFYjLUgbxW8WSwWBgcH+fWvf01AQABbt25l3bp1Y36HTIzOzk6jzzIyMoLZbKaxsZEf//jH\nvPnmm+zfv5/Tp09z9epVwsPDcXV1JTg4GIvFQklJCenp6VRWVnL69Gneeecd2tra2Lp1Kw8++CAw\n8zdNaGlp4ezZs4SEhIzp97399tv87Gc/Iycnh5KSEvLy8igvL8fZ2ZmAgADmzp1LYGAgxcXFFBYW\ncuLECQ4ePEhNTQ1BQUH87d/+LeHh4VP87mYG2229tnajpKSErKwssrOzaWxsxMPDw1hrLzY2luzs\nbPr7++nt7cXBwYG+vj7eeecdDh48SE9PD9/+9rcJCQmZ4nc1vVy6dImrV68a15O282Dv3r28+uqr\nHDt2jKysLNLT02lpaSE4OBhfX1/Cw8NxdHQkOzubzMxMMjMzOXDgAKWlpXh7e/PNb35zWp4HCt1k\nWjCZTHR1dfHqq69iMpnYsmULa9asAWDfvn3k5OQQHh7OmTNn6O/vJzg42Ji1ZgveYmNjjS3H5dOz\nfck0NDTwT//0Tzg7O3Pffffxla98BV9fX06fPk11dTURERFjgrUFCxYwMjJi7F64cuXKcTu72Z47\n0xf3Ht1h7Ovr46233iIuLo7t27cbobCvry9tbW2UlJTQ2NiIk5OT8SV+/Phx9u3bR19fH3/2Z3+m\nrd7/YHS9Zmdn09LSwjPPPMO6desICwsjISGB2NhYOjo6KCgowM7OjoULFwI3R8ciIiKoqKggISFh\n3C1hcpPVauX69ev8+Mc/pq+vD0dHR86cOYOnpychISFG4GALgTs7O3n77bdZtWqVcYELNxe4zczM\n5Dvf+Q7r168nNTV13P8VGBioW2BuYfHixVy4cIHCwsIPXFPMFrw5OTkREhKitds+xP79+8nKyiI1\nNdWYlWNvb09oaCj19fUUFRXh6upKeHj4mODNtvFPc3Mz7e3ttLS0UFFRYYQ8NjP5u2wq2GYhZmVl\nER0djdlsvmXwdunSJUpKSqirq+Ouu+7CZDLh4uLC2rVrSUtLM3YSVOA2sV566SXOnDlDdHQ0Hh4e\nRn/xxRdfpK+vj7i4OGJjYzl//jyVlZU0NjYSGBjInDlzSExMJDIyksuXL9PZ2UlPTw/Lli3j8ccf\nH3O78Ez+vLq7u/nmN79JRUUFwcHBRhBz8OBBfvvb35Kamspjjz3G3XffzcDAAGfPnqWiosJYViA6\nOprVq1fj6+tLQEAA8+bNY/PmzTzyyCNjbrOTT66hoYGMjAzi4uLGhEC7d+/mZz/7GRUVFdTX11NR\nUWEMms+ZM4fQ0FBSU1NpamqiqKiI06dPc+zYMRobG4mKiuKv//qvp2UINJU6Ozv567/+a1pbW5kz\nZ45xTf/73/+eXbt2ERsby4YNG0hJSaGjo4Pq6mrq6urw9/cnOjqapKQkUlJSMJlMODk5ERYWxv33\n38+WLVum7dIOCt1k2igpKeHgwYM8+uijxpfv66+/zp49e9i0aRMPPvggAwMDZGdnY7FYCAgIME5S\nd3d3IiMjgZk/Qna7mUwmLl26xCuvvIKHhwdbt25lw4YNeHp6UlRURE1NDdevX6e6upo5c+aMCd6S\nk5Pp7e0lISHhlhfZo/+Pmcz2/goKCrhw4QIHDx7k8ccfJyEhAbh5jLq5uRETE8PVq1eprKwkKyuL\n06dPc/jwYXJycnBwcJi2ozVTxWQy0djYSHFxMdXV1djb2/PEE08A7609FhgYSFBQECUlJZSVlRET\nE2PsDuvv78+qVas+9Nic7Wzr71itVkpLS1m+fDkjIyPk5uaOC94ALly4wLFjx/D29iY+Ph4XFxda\nWlqMBW7XrFlDcHDwh250I++ZqMX85WaA3NnZycsvv8zZs2fHrOloMpkICAggKiqKsrIySkpKxgRv\ntnXC4OZC2W5ubsYtpbZd6uT2uHHjBi+++CIZGRnG9+Stgrfk5GRyc3NpaGgYs7mCo6OjEUKPnqki\nn11nZyeVlZUUFBQwMDBAaGgobm5u7Ny5E7PZzLPPPsvnP/95li1bRmpqKoODg5SUlHDx4kXi4uLw\n8fEhIiKC1atX89BDD7Fx40aWLVtmBE8zPXCDm4PObm5uFBUV0dTUhI+PD4GBgbzzzjtERESwbds2\nkpOTCQsLIzU1FT8/P2pra6mtrSUiIoKQkBDc3NyIi4tj0aJFxgYi7x/klo/ParUyNDTEt771LfLz\n83FyciIuLg64uTnF66+/zqpVq9i+fTsPP/wwfn5+XLhwgZKSEq5cuUJ8fDzBwcEsWbKEpUuXEh0d\nzZIlS3jiiSfYsGGDls+4hf7+fvr7+8nLy6Orq4vQ0FAcHR3ZvXs3SUlJbN++nbS0NOLi4li8eDF2\ndnaUlpbS0tJCcnIy7u7u+Pn5kZaWxpo1a1i+fDnR0dHTegBSoZtMGzdu3KCjo4Nnn30Wk8nE4cOH\nef3111m3bh2PPfYYMTExWCwW8vLyaGpqoru7m9DQ0HHrtOmi7rMZGRnhyJEjVFdX89hjj7Fy5Urg\n5ppk+/btY/369aSkpFBcXMzZs2eJiIjA39/fqPfU1FStJwSUl5fz8ssvc+nSJZycnHjiiSdwdnY2\nOpVWqxUPDw/i4uKYO3cuQ0ND2NnZ4efnx913380zzzxjhEVy08DAAP/5n//JoUOH6O7uZt68eSxe\nvBjAqFPbxbSzszNFRUWEh4eTkJBgBD66JezDjd5ZtKSkBLPZzMqVKzl37hwlJSV4eHiMCd78/Pxo\naWmhqKiIzs5OmpqaOHjwIKWlpTz55JPGSCSobX6/27mYv2Acx3PnziUzM5Pq6mqGhoZYsGCB8XNf\nX1+io6PHBG+2zj/cnPmwZ88e7rnnHp599ll9t00Cs9lMbGws5eXlFBcX4+DgwNy5c8cEb8PDw7i4\nuNDR0cHly5dpbm6msLCQDRs2jPld+owmlouLC9HR0dy4cYPjx48zNDSEn58fBw8eZOXKldx7773A\nzX6kr68vc+bMMdb1tFgsY76vzWbzmPWBYXZ8XmazmcjISHx8fDh16hQtLS2YzWaOHj3KAw88YKwT\nabFYcHJyIjQ0FAcHB/Ly8rh27dqYGYGjd2GfDXV3u9gW6V+wYAE5OTkUFhZib29PQkICx44dw9HR\nke3btxMbG4u3tzfz5s0jKSmJhoYGysrKMJvNxMXF4ebmhr+/P7GxscydOxcfH59xG6XJTS4uLkRF\nRQFw4sQJent7cXJy4uDBgzzxxBPMnz8fuHmcu7u7ExUVRV9fn7HUVFpamvHzO+U8UOgmU+JWa3b4\n+vqyfPlyzGYzFy9eZOfOnQQFBbF161YjfLBdCM6bN4/8/HyWLFmie+Rvg9zcXEwmE8888wxw8xbf\n3bt3s379erZs2cLSpUupqakxdqIKCwsjICBAO0GOYmdnh729PaWlpXR1dREQEEBsbOy4zqWLiwvh\n4eGsXr2atWvXcs8995CQkDCtdtyZLuzt7fH09KS3t5fGxkb6+vpISkoygvf370p67NgxnJ2dWb16\ntfFzm9l8bH4YW714enpSW1tLY2Mjn/vc54iMjKSyspKysrIxwZvJZCIwMJDr16+TnZ1NbW0tVquV\nrVu3GjubTveO0FS53Yv5z3ZWqxWA0NBQYmNjycjIoKamZlzw5ufnR3R0NGfOnKGwsJD+/n4iIyNp\nbm7m0KFDNDc3s27dOqOvoeN5Ytna7NE7z/v6+hIfH09BQQGlpaVjgreRkRFjVmd+fj4A4eHhpKSk\nGLPJ5fZxd3cnLCyMoaEhjh8/Tnd3N93d3Xz+85/H29ub4eFh4/Nxc3MjMDCQvLw8qqurSUtLw9vb\ne9Z/F5vNZsLDw/H19eXUqVO0trZiNptZv349/v7+DA8PGzNt7e3tCQoKorKykurqahYtWoSvr++s\nr8OJZrsOXbRoERkZGZSUlGCxWCgtLWXVqlUsWbIEuNn+m81mvL29iYyMpLy8nLa2Nu666y6cnJw0\no/8TcHV1JSwsDDs7O44fP05HRwf29vZ87nOfw8XFxVgvEm6uoRoaGmqsHb569WpcXV3vqPNAoZtM\nCZPJRH19PbW1tYSHhxuj/LaT6/z58+zfv5/NmzeTlpZmdHKLi4tJT0/nmWee4b777tPFxgQYfQFh\nG0WOiopi7dq1ODg4cObMGXbu3MmCBQt44oknjF15enp6aG1tpaenh6ysLNauXTutp/VONtuMCQcH\nB+rq6rh+/bqxDt772T4DbSN+040bN4y6eP/uaCEhIbi7u3P9+nXq6+txd3cnJiZmzEYTtjAjOzub\nVatWkZiYOO2/jKeT0XV96NAhHBwc2Lx5M+7u7tTW1lJaWoqHhwfBwcE4OjoaAybz589n/fr1rFu3\nzuigzobbhT4pLeY/OUbXR0hIyEcGbzExMdTW1pKfn8+BAwc4ceIE58+f56mnnjJml7z/98pnZzKZ\naGhoYPfu3cyZMwdXV1fg5mzOhISEccGbLdBpampi//79bNq0iW3bthmBm86F22908JaRkUFPTw9+\nfn4kJCSMae8tFgve3t50d3dTXV3N6tWrx+zsOJuZzWbCwsLw9/cnNzeXa9euYWdnx5IlS8bM3LfN\n6uzt7eXMmTOsXLly2q5ZdSez9TO9vb1ZvHgxp0+fpq6ujqGhIeLi4oiLixsThsLNULmtrY2ysjKi\noqKIiopS2/MJ2a6VzGYz+fn5XLt2DS8vL+Li4sadB56enly4cIHa2lruvvtuvL29p7r4n4hCN5kS\nvb29vPDCCxw/fpzw8HAiIiLGXFi3traSkZFBcnKysaBlS0sL+/fvx9PTk8cee8zYClijCp+e7YK4\nvb0dJycnzGYzVqsVV1dX7O3tMZlM5ObmUlBQwJe//GXmzZtnvDY7O5tLly6xadMm0tLSSEpKmsJ3\nMnVG3xo2Ory03doUFBSE2WwmNzeX7u5uwsLCxn1R6Ph9z4EDB3j11Vfp7+/H2dkZHx8fAONL187O\njuDgYLy9vbl8+TJZWVk4ODjg7e2Nh4eH0X688847tLa28vDDD2s27IcoLS0Fbtavo6OjEfhYrVbs\n7e05e/YsVVVVrFixgnnz5uHq6srZs2cpLS3F09PTCN5MJhPBwcH4+/sbx7fWU3qPFvOfGh83eIOb\naz7eddddjIyM4OPjw9y5c/n85z9v3DKnvsbEGj2gsmfPHo4ePcrQ0BCRkZHjgrfCwkIKCwvp7u4m\nJiaG+vp6Dh8+TENDA2vXrtUsxElkq2N3d3dCQkIwm83U1dXR09NDdHS0sfHT6AGXsrIyampq2LBh\nw6zYuf6jjB5oDQ0NJSAggMrKSs6fP09AQACRkZGYTCaGhoaMAcUTJ05w7tw5Nm/ePG5ZHZkYo4O3\nRYsWcfLkSXp7e7G3t2fNmjXGYJjtebb1P7Oysli0aBHR0dFT/RbuKKOXM7HdPWGbpBAZGWmsGT56\n9uyJEyfo6uri0UcfveMmeih0kynh6OiIh4cH9fX1nDhxgrCwMCN4g5szXfLz88nPz8fDw4OWlhZj\nO+DHHnvMWOASdLHxWZhMJi5cuMCf//mf09jYyIoVK8asmwI3G7jm5ma2b99uzKqwrdu0ZMkSnnzy\nSWJiYoDZd1Eyup4GBwfp6ekx3r9tNGz0KE56ejpXr14dE7zpIuE9jY2N/Nu//ZsxKn7ixAm6u7sZ\nHh4mNDR0TMgQFBSEn58f7e3tnD59moaGBjo6OqisrOTIkSOUl5ezdevWMTtqyljHjh3jBz/4AXl5\neVRWVuLt7Y27u7sRojk6OuLm5saxY8cIDQ1l3rx5hIeH4+bmZgRvXl5eBAUF3XLdEh3XWsx/Ovg4\nwZutY+/o6MjChQtZuXIlS5YsISIiAtCMzYlmq89Lly7R2NhIVVUVIyMjlJWVMTw8zJw5c4wLKh8f\nHxYsWEBpaSmlpaXs37+fkydPGrMQR7fxanNuj9H9lNGDjB4eHgQGBmKxWCgqKmJoaIiAgAB8fHyM\n57e2tnLw4EEsFgv33XcfHh4eU/lWpsT7+8aj/267fdTPz4+ioiLOnTuHk5MTc+bMMdr/wsJCDh8+\nTGBgIJs2bcLJyWnS38NsYBvssq1JuGTJEjIzMzl37tyY7+6hoSEjBDp9+jTV1dVs2LBBA7wf4cPO\nAzc3N4KDg7GzszNmvHl7exMYGGh89xYUFPC///u/hIeHs27dujF3uNwJFLrJpLOddFFRUfj6/n/2\nzjs8rurM/58Z9TaSRm0kjTSqVreqe8W23AHH2BjbsTE4gexuYBeSJyFtWdpjUpdN2GxCniQ0Y8C9\nV/XerC6rS5ZkFduqVq+/P/y7l5ELGJCRkc7nHx6smXnmnHvn3Pd8z/t+XzWXLl0iMTFRFt4ArK2t\nsbGxoby8nPT0dLKzs+no6GD79u2sXr0aEGLFRKFQKMjPz6e0tJSmpiaioqJk3xSlUsnly5cpLi6m\ns7OTmTNnyifMpaWlrFy5Ur5m0mdNF/Q3YbGxsXz00Ufs3buXuLg4mpqaUKvVcpaW5FugL7xptVqs\nra2n1Zx9EZ2dnWRlZeHg4EBoaCjGxsakpaWRmppKTU0NY2Nj2NraygGnk5MTarWa9vZ2Ll26RFlZ\nGWNjY7i7u7Nu3TqWL18OTD8x+F5ob2/nlVdeAW7en8PDw5w8eZLS0lJ6e3tlg1sXFxdycnIoKysj\nPDwclUqFm5sb5ubm1NTUkJWVJXcYFKLE7Qgz/weDexHepMzCO2UQinmeOKRnZ2VlJXv27KGoqIiB\ngQG0Wi0NDQ1UVVXR29uLl5eXLLxZW1uzaNEi2UtpxowZbNiwQazx3wDS9eru7ub69evyuiOJDlZW\nVri4uDA0NER8fDwNDQ0MDQ3h5OREfn4+58+fJy8vj61btxIeHj7Jo/nm0Y8V6+vrqaqqorS0lBs3\nbmBhYYGxsTGGhoZypnhqaiqZmZlcvnyZ3t5ezp49S0JCAv39/fzkJz8R5bkTyN1EIElYlkpNk5OT\nKSwspL+/n9DQUFkMvXjxIqdOncLS0pLvfOc7ooPs56D/O6isrKSqqorBwUEAeZ2XYh+lUkliYiKl\npaVcu3YNIyMjzpw5Q3x8PH19ffzoRz/6VmbMCtFNcN+508ZA+jep82VpaSkJCQnjhDedTkdISAgh\nISHMmTOHdevWMWfOHECcOk8UUnekefPmUVFRQW5uriy8SQGVt7c3+fn55OXlcerUKS5cuEBdXR1b\nt26VA97phn7Z3IEDB3j//fcxMDAgPDxc7khVXFyMm5sbjo6OwM2HiiS8JSUlUV9fj6enpygT0MPG\nxoaenh4uXrzIpk2b2LJlC56enrS2tlJZWUlqairp6emyKGxvb49Go8HOzo729nauXr3KmjVrePTR\nR/Hw8ADEWnE3zMzMCAgIIDExESMjI5YtW8aKFSvIyMggPT2djIwMmpub8fHxobu7m8LCQkJCQnBy\nckKpVOLm5oaJiQlFRUVERETg4+Mz2UN6IBFm/g8O9yq86TdjEUw8CoWCpqYm3njjDWxtbdm2bRs7\nduxg3rx5BAQE0N3dTVpaGj09PXh7e2NmZsbY2BgmJiYEBgYyb948wsPDcXd3B8Qafz+R5ra6uprf\n/e53HDp0iPj4eGpqanBzc0OlUgGfebyNjIyQmZlJbm4uFy9eJDY2FiMjIx555JFpeWCuf28eO3aM\nv//978TExJCdnU1SUpKc/ezl5YWhoSHOzs44ODhw6dIlampquHr1KgMDA4SHh/Pkk0/i6uo6ySOa\nOuhfm9TUVJKSkmS7DY1GIwtv1tbWREREkJKSQnFxMYWFhZSVlREfHy938P3JT34ifPY+B/0906FD\nh3j77bdJSUnhwoULXLp0SS6xhs+EN0NDQwoKCigtLaWyspKWlhZ8fX155pln0Gq1kzmcr4wQ3QT3\nFenh2tTUxLVr18alnOsLb3Z2dhQVFZGUlIS7u7v8g7KxsZE93+7kEyH46uibdZuYmBAZGUl5eTl5\neXmy8CZ14Fy0aBFjY2OoVCoCAgLYuHEjDz30EDA9T5il8Z45c4ZPPvmExYsXs2PHDqKjo5k7dy6x\nsbFcu3aNgoICvL295YeJJLwNDg5SWFjIunXrZO+a6Y50H5mampKcnExDQwOLFy9Gp9MRGRlJaGgo\nIyMj1NfXk52dTWZmJu3t7ZiamhIYGIhGo6GpqYn4+HgsLCxwd3eXfQkFd8bR0ZGAgADOnz9PZWUl\n69evZ/PmzXh6elJbW0tmZiaJiYkYGhpSV1fHjRs35LXAwMAANzc3Zs+eTVhY2GQP5YFFmPk/WHze\n9dAvH5qOz7VvkpSUFNLT03nsscdYuHAhcDMmcXJywt3dna6uLlJSUhgcHESn0417TkqCqMhCvP8o\nFArq6+t57bXXGBoaIigoCDMzM3Jzc8nMzCQgIECOzSWPN6VSSU1NDS4uLnz3u99lw4YNcjn8dIvf\npXvz8OHD7Nu3D39/fzZu3EhYWJh8aFVYWIhSqcTf31/OeJOEN6VSybZt21ixYsW0LMu9n0jX5uDB\ng/zjH/+grKyM8vJykpKSGBkZwdHREUtLS+Bmpq1UatrU1ERvby+urq7MmzePJ554QoihX4A01zEx\nMXz44YfMnDmTlStXYm9vT05ODsnJyXh6esqHiubm5rJfcHV1NY6OjjzzzDMsW7bsW52oIEQ3wX1F\noVDQ1tbG888/T21tLR4eHuNaXesLb5aWluTk5JCWliYLbXf7TMGXR9pEdHd3o1Qq5aYJkvBmampK\nVFSUnPHW3Nw8TngLDg5m3rx5hIWFyaLoVA+gPu9Etrq6Wg6iNmzYgE6nY2hoiF/+8pd0dHQQFhZG\ndXU1eXl5twlvHh4erFmzRi4/FXz2u7axsaGyspKSkhJ0Oh1ubm6y58ns2bPlLl8ApaWlJCUlr8mV\nKgAAIABJREFUUVxcjIuLC4GBgVy9epX4+Hisra3l9wrujqOjI0FBQcTExJCcnIyfnx+RkZE89NBD\neHp6ys1UpC6aM2bMwNDQUBbepEwHIVLcHWHm/2Bxt+tRWlqKubk5M2bMEHN8n4mPj6e6upqnn34a\nCwsL2c4CPrMXSU9Pp7y8nIGBAby9vTE1NRVC2zeItNYcPXqUrq4udu/ezcaNG+UmLnl5ecTHxzNz\n5ky51EvyeOvv7ycrK4vo6GhZkJiujXUKCwt59913iYyMZMeOHQQHB+Pl5UVQUBCurq7k5ORQUVGB\nra0tOp1OznizsLCguLiYRx99FAsLi8kexpQkOzubvXv3Mnv2bL73ve8RHh7O0NAQsbGx9PT0oNVq\nZbHT2tqa8PBw0tLSaG9v56GHHmLVqlWyMCe4O9JacvjwYezt7dm1axcRERFERkZia2tLSUkJ8fHx\neHt7y8Kb5PHW19dHSUkJjzzyyLc+SUGIboL7jpmZGV1dXWRlZXH16lVcXFzuKLx5eXlRWFhIZ2cn\nqampODo6yiVigq+PQqHg8uXL7NmzB1NTU9zc3O4qvF28eJHi4uJxGW/6Att0CHz1x9vf3z9OvBkb\nG6OwsJD09HS2bNmCv78/o6OjvPzyyzQ1NbFz5062b99OX18fhYWF5Ofn4+npOa7UVBjh3o405w4O\nDqSkpAAwd+5c+Tq8/fbb5OXlsWrVKjZu3Ii/vz+NjY1UV1cTHh7OvHnzsLe359q1a5w/f17OHJrK\n9+lE4ODgIAtvWVlZuLm54erqiouLC5GRkQQGBjJ79myioqLk4P/WORVz/PkIM/8Hi1uvh7u7O2lp\naVy7do2oqKhvfXD/oCLFe+Xl5ZSWlqLT6fD09LztvnZwcKC1tZXm5mbKysoYGBggIiJi3GcI7i/S\nHB87dgxfX1+5PBQgMDAQMzMzCgsL7yi8ubi4MGvWLIKDg2/7vOlGdnY2eXl5bN++HR8fH/n+NTEx\nQafToVKpyM7OBmDevHnAzUZcOp2O6Ojo27rdC74+kgiUmppKR0cHu3btwsvLC61Wi6enJ6Ojo8TF\nxdHX13eb8BYSEkJeXh6PPfaYfOgouB39A0Iptvnkk09Yt24dgYGB8kGLl5cXNjY2dxTezM3Np1SS\nghDdBN8I4eHhDAwMkJSUxLVr18YJb5LnjUKhID4+HkdHR9ra2vD19R3XpVTw1dBf+Kqrq7lw4QL1\n9fVYWVnJHmP6HXskQS41NZUrV65QW1vLnDlzZNFpOgRO+pvbv/71r1y9ehVXV1fZ2FyhUNDf34+P\njw/z5s1jbGyMv/zlL1y6dInHH3+cRYsWYWRkJGcJjY2NkZCQQFBQEPb29pM5tAca/Qd0Xl4eRUVF\nBAQE4OjoyB//+EeSk5NZvnw5GzZswNPTEy8vLxYvXkxkZCRRUVHAzeYKNjY2dHd3s2LFim91Kvo3\nib7wdvHiRXQ6nRz4ODg4oNFohEnw10SY+T9YSPM5OjqKm5sbLS0tFBcXM3fuXLFOTxBS/DE0NISB\ngYE85/39/aSmptLd3Y23t7e8To+OjgI3r01aWhoAvr6+JCYmYm1tjbe3t/gd3Eek69Xb20tXVxdK\npZL09HSCg4NlMUISjXx9fccJb6GhoXKpqZWVlexxNZ2yc/UFYWncJ06coL6+ntWrV2NnZ3ebaKxS\nqSgvL6egoIDZs2fLIpuBgcG3rjvjg8ytIhDcFES1Wi3z58+XRSArKyu0Wi3Dw8N3FN5sbW1ZtWqV\nEEM/B/09VG1tLfX19YyNjVFQUEBISAguLi7AZ78XDw+PuwpvZmZmUyb2FKKbYEK50wmktNDNnDlz\nnPDm7OyMnZ2d7M1RXl5OfHw8zz77rOx5IPh6SAtfbW0tDQ0NhIaG4unpSWZmJiUlJVhbW48T3qRr\nMTQ0REZGBhYWFlRUVDBjxoxp1QpbuoffeustkpKSuHLlCpaWljg5OcnCm729vTx3FRUVHD58mMDA\nQDZv3jwuSyIzMxNfX1+uX7/O+vXrhS/HPWBmZoaZmRmZmZmo1WrOnTtHeno6K1asYNOmTXLgKvkR\nShtkSazQaDTMnj1b3gAI7g194S0nJwcPDw9h3D/BCDP/BwuFQjHOTLuxsVFsqCYQhUJBdXU1hw4d\nwtHRURbX7O3taW1t5eLFiygUChwcHFCpVPI939DQQEJCAgsWLGDhwoWkp6fT1NTEggUL5GewYGLR\nb5rw1ltv8emnnxIXF8eVK1fw8PCQfdn01ydJeCsuLub8+fOEhITcJlhP1TXsVjFR/xnZ19cn36dX\nr16lsLAQnU6Hj4+PnGwgvdbc3JyWlhZKS0tZuHChEPzvA/oiUHZ2Nunp6ZSUlJCXl4eFhQWRkZHj\n7msLCwu0Wi0jIyPExsbS39+PRqOR1y/xbL47+mXkR44c4c9//jNxcXGcPXuWtrY2vLy8xlk43Cq8\nlZeXExsbi6+vLxqNZjKHMuEI0U0wYUiLWltbG/X19dTX16NSqcYFSCEhIbLw1tjYiFqtRqPRUF1d\nzZkzZ2hubmbRokXyRm86nZDdDxQKBTU1Nfznf/4nlZWVREVF4e7ujqurK9nZ2XcV3i5dukRVVRUv\nvvgiISEhchbRdKKhoYH3338fAFNTU/Ly8rC2tsbZ2Vm+p6W24ZcuXSIxMZENGzaM6+J44sQJGhsb\nefXVV1m1apUQgb4EZmZm5Ofnk5WVRVNTEytXruQ73/nOuJPiW0uSlEql/Dfh5fbV0BfesrOz8fT0\nlDt5CSYGYeb/4JGVlcXZs2dxc3Nj5cqVQtiZIEZGRjh06BAxMTEMDw/j7OyMSqXCwMAAS0tLWlpa\nyMzMpKWlRfbuLCsr4/Tp0xQXF7N06VKCg4OprKykrKyMVatWYWZmNtnDmpIoFArq6up49dVXZR89\nKysrmpqauHTpkpx1rm9JIglvxsbGFBQU4OnpOW06WUtrc2JiIiYmJvKB6rvvvsuJEyeYM2cORkZG\n9PX1kZycTHV1NV5eXjg6OsrldlIMk5iYyLVr13j44YeFR9h9QLpWBw4c4G9/+xvFxcUUFxfT0dGB\niYkJM2bMkEX/W4W3sbExYmJiGB0dJTQ0FKVSKZ7Ln4M0N2fPnmXv3r0EBgayYMECtFot1dXVFBQU\nyA0T7yS8mZmZUV9fz8qVK6dc+a4Q3QQTgv4J2e9//3tOnz5NQkICGRkZ2NnZYWNjg7GxMQqFgpCQ\nEEZGRkhOTiYpKYm8vDxOnz5NZWUlmzdvZvbs2fLnioXt6zEwMMC7776LiYkJmzdvxtvbG6VSiZOT\n0zjhzdzcHK1Wi6GhIfX19Zw6dYrh4WHWr18/zk9oOl0PlUrF8PAwJSUlLF68mP7+fjIyMlCpVOOE\nN4ArV66Qnp6OjY2NbPiclZXFhQsXcHR0ZNasWeNMoAVfjIWFBS0tLVRUVODr68uOHTtwcHD4wowr\nMcdfH33hLS0tDZ1OJ5cDCCYGYeb/YNHf38+pU6f4xS9+ITJNJhClUomnpyf9/f1yxogkvDk4OGBv\nb8/g4KCcfRITE0NMTAy1tbVs2bKF5cuXAzc3cGNjY6xfv14cqEww+rHdp59+Sk9PD7t37+bxxx9n\n8eLFABQXF4/rxn6r8DZjxgxmzZo17Q5ojx07xt///ndMTEwICAjg4MGDHDt2jICAANn3ztnZmeHh\nYfLz82lubsbe3h4nJydZcMvNzeXs2bPodDqWLl0qSkonEP14MT09nQ8++IA5c+bw1FNPMXPmTHp7\neykpKWF4eBgfHx85TtcX3pydnTEyMhL+el/ArXvEgwcP4uTkxNNPP83cuXOJiIjA2tqa/Px88vLy\ncHV1xdXV9Tbhzdvbm6VLl8oekVMJIboJJgT9E7LR0VHmzp2LRqOhsbGRzMxMuQuJiYkJCoWC4OBg\n7OzsuHHjBh0dHTg6OrJp0yZWrVoFiFKmr4M0d11dXfT09HDw4EGWLFkyrvudgYEBjo6OcuekzMxM\nampqaGlp4dixYxQVFbFx48ZxJ5bT6Xrol9rm5OSgVquJioqipqaG/Px8rKys5HbWAHZ2dtTV1ZGe\nnk5VVRUZGRmcO3eO/v5+nn/++XGNQwRfjDT/Wq2WnJwcRkdHWb9+/bhsTMH9xcHBgYCAAOLj4wkI\nCMDb23uyv9KUQ5j5Pzio1WoefvhhkY18HzAzM8PLy4uenh7i4+MZGBiQS7UcHR0JDAxEp9PJ5vKh\noaE8/PDDREdHAzc7ncbGxhISEsLs2bPlDHPBxCCV87a0tJCbm4ufnx8rV66U/x4UFIShoSHZ2dnk\n5+ffVXiTBInpdEBrYGBAb28v8fHxFBQUkJSUxJo1a+SsfGkudDodPT09srg8ODhIW1sbKSkpnDx5\nkt7eXl544QWx/kwgt8aKCQkJ9Pb2smvXLnx8fHB3d8fd3Z2+vj4SEhLo7+8f1yVZer+lpSVBQUFC\ncPsCpLk+c+YMtbW15Ofns3btWgICAuTfgbe3NzY2NmRlZZGbm3tX4W2qCs9CdBN8LfQfrgcPHuTG\njRs8/fTTrF+/nrlz5+Lu7k5DQwMpKSlyRyNJqPD09GTWrFlER0czf/58/P395c8UHdq+OgqFgqam\nJl544QWuXbtGf38/Tz31FCYmJoyMjMgBq+R95efnR1VVFSUlJRQXFzM4OMi2bdumtQAqjdfBwYGi\noiIaGxvZtm0bGo2GS5cuUVBQgEqlkoU3IyMjXFxcGBwcJCsri4GBAXQ6HS+++CKurq6TPJpvH/pm\nt6WlpZSVlWFoaEhgYOC0uxcnE0dHR5YvXz6uA51gYhFm/g8OIoPq6/F5YouZmRne3t53FN6kLo5z\n5sxh0aJFREVFyRn2qampHDlyhJGREX74wx+Kje8EMzY2RmdnJ8899xwFBQW0t7ezZMkS3NzcGB4e\nBm6uUQEBARgZGZGVlXVH4U2f6fSMVqvV+Pv7k52dzeXLl/Hw8OCRRx7B3d0d+Cx+ljLhTExMKCws\npLS0lKysLC5fvoydnR0/+clP0Gq1kzyaqYV0H+7fv5/k5GS6uroIDAxkzpw58lpla2uLs7OzLJze\nTXgTe9IvZmxsjMuXL/Pb3/6Wuro6hoaGWLp0Kfb29uMa5EjdSiXhTavV4uLiMi188oToJvjKSA/b\nuro6xsbGyM3NRaPRsG7dOvk1jo6OuLi4UF9fT0pKCpaWluOEN2NjY0xMTDAxMRn3mYKvR1NTE/X1\n9bJngYeHB+7u7ncMjuzs7Fi6dCk+Pj489NBDrFixQi7xneoC6J02CdJDVv/09tSpU1hZWbFq1SrM\nzc0pLy8nPz9/nPBma2vLrFmzmD17NuvWrWPBggVTMj36m8TIyAhnZ2diYmIYGhpi9uzZwmvpG0by\nT5pO2QvfNMLMXzAVkDxkq6qq7liObmZmho+PD52dnSQmJjI0NDSuucLY2Jh8KDgwMMCf/vQnkpOT\n6e/v55e//KUQJe4DCoUCU1NThoaGyM3Npa+vDx8fH/z8/OQ1SYqJ/P39MTIyIjs7m5ycHLy8vOQO\npdMRaV4yMzOJi4vD0dGRxsZGbGxscHZ2xtzcfJxwY2xsTGBgIKGhoURGRuLp6cnDDz/M2rVrcXR0\nnOzhTEk6Ojo4cuQI2dnZ1NfX4+7uTlhY2LjrInk1S8Lb4OAgnp6ewhLmSyLtl6ysrEhNTaWvrw93\nd3dmzJhx21oiCW+5ubkkJSWN61Y6lRGim+Aro1AoaG9v54UXXuD48eOyqu3m5iar2kqlEnt7e5yd\nnWXhzcrKSvbEunVBEwvcxGBnZ4ezszPd3d1cuXIFExMTfHx87mhALJWburi44OTkhK2tLTD1BVB9\nQfHgwYMYGhpiYGAgz5F0L0oGwQ0NDSxatAidToeFhQWVlZW3CW8A1tbWmJmZCXFogpAaKixcuJCg\noKDJ/jrTFrE233+Emb/g20x3dzdvvvkmZ8+excPD445Z3qampmg0GioqKrh06RLDw8M4ODhgbW09\nrsSoqamJM2fOoNFo+Pd//3chuN0npMOUkJAQjIyMKCoqoqysDF9fX5ycnG4r+/L390ehUJCbm0tA\nQACenp6TPIJvnls7S5uZmeHi4sLChQsZGRnhwoULKJVKXF1dZeFN/9DKzs4OV1dX/Pz8cHR0xNTU\ndDKHM6UxNTXF3d2dGzducPXqVUxNTQkLC5Otjm4V3iTvybGxMUJCQkTcc4+MjY3Jc+nj44O9vb3s\nGe7m5nbHElIvLy9MTU2pqanh4YcflhuRTGWE6Cb4WpiZmdHT00NzczOtra24ubkRFBQkCzbSj8ve\n3h4XFxeuXLlCbGysfOIpvDkmHunhbm9vj62tLR0dHWRmZmJqaoq3t/dtJTR3e6hM9YeNNL7f/e53\nnD9/nvz8fDIzMzE3N8fExETuICUJaBcuXMDDwwMPDw/c3NzGCW/W1tY4OTndUUgWfD0MDQ2ZO3eu\nLLhNx3JnwfRAmPkLvs0YGxtjbGwsx3nSZutWrK2tuXTpEjU1NVRXV9PZ2UlISIgsMkub4Pnz5zN/\n/nzhczWB3Pr81BeEpEy2/Px8qqurcXd3x8HB4bbNcmBgIBEREURGRk7WMCYN/cPakpISSkpKsLe3\nJzQ0FGdnZ5ydnens7CQ+Pv424Q2guroaAwMDubpHcP+Q7mu1Wo2joyOtra3k5+czODgoZ7vdKrxJ\n3XlXrlwpZ+AKbufWyodbS0M9PT1Rq9VkZWVRUVGBk5OTXEIKn60lvr6+LF++fNpUBQnRTfCVkX50\nYWFhdHd3U1VVxeXLlwkPD8fGxkZ+OEk/Ljs7O5ycnKiqqsLf358ZM2ZM9hC+9UjXYHBwkP7+fgYG\nBoDPvGkcHBxwcHCgra2N+Ph4DA0N8fLymrImlV+W+vp63n33XQwMDFCr1VhaWnLy5Elyc3Pp7OzE\n2dkZQ0NDnJycSE5Oprm5mbCwMCwsLHBzc8Pc3JyamhqSkpJwdnaWzaAFE4u0GROCm2AqI8z8Bd8G\nBgcH5QPTW+0YPD09sbS0pLKykoSEhNuEN+l1xcXFaDQa3N3d8fX1vaNvpImJiYhVJhApJu/q6pK9\nI6UYXcq28vf3R6lUkpaWRmVl5V2FN2mNmk62A/qC27Fjx/jggw9kfztHR0cMDAywsbFBq9XS2dlJ\nXFwcSqUSrVaLmZkZRUVF/OlPf6K0tJT58+dP6UqSb5pb70N9/2q4+Wx1dnamtbWVlJQUent7CQ0N\nvU14s7GxITQ0VFg7fA76v4PS0lKKiopITEykv7+f0dFRWaz08vLC1taWpKQkysrK0Gg0svCmnxk3\nnTL6hegm+MooFAqGh4dRKpWEhIQwNDQkZwvNmTMHS0vLcT9OKftq1qxZhISETPK3/3bS3d0tL1DS\n3NbW1vLOO+/wySefcPLkSZKSkjA1NcXS0hJzc3Ps7e1xdHSkra2NuLg4jIyM8PLyEqbR3DxxDwwM\nJD4+nrGxMVavXs3atWspLS0lLS2NlJQUamtr8ff3p7W1lcrKSubMmYO1tTVKpRI3NzcMDQ1pbW3l\n4YcfRqVSTfaQpjTTJbgXTF/Euix4kDl58iR/+9vf6Ovrw9TUVLaj0I8HdTodKpVKFt5cXV3l8lCF\nQkFtbS3Hjh0jKiqK7du34+fnB4hDlfuJFC9WV1fzv//7vxw7dozk5GQSEhIoLS3F3t5e9mcLDAyU\nhbeqqirc3d2xt7e/o9H5dLpe0lgPHTrEvn37iIyMZMuWLURERIwTeKytrXF1dZUz3tra2igvL+fc\nuXN0dnbyr//6r9Mms+ebQH+fmZGRQUxMDIcOHaKgoIC+vj5cXV3lg3UXFxdaW1tJSkq6q/AmxNC7\noz/Xhw8f5r333pMF+tTUVPLy8lCpVHIjEUl4S05OpqysTM4GnQ5NE+6EEN0E94R0ijA0NMTo6Chd\nXV2YmZmNW5yCg4MZHR0lNzeXtLS0ccKb/oImeWaJAOvLsWfPHurr6/Hw8JANPquqqnjttddobW3F\n1dUVW1tbampqyMnJoaurC7VajVqtHie8JSQkyPX04hT5ZrOPgIAAzp8/T0lJCdHR0Tz++OOEhITQ\n0tJCfn4+MTEx9Pf3c/XqVQAiIiJk02cPDw8WL14sslMEAoFAMGWpqanh97//PTdu3KC0tJS4uDhu\n3LjB8PAwLi4u4+JBnU6HlZUV1dXVJCQkYGJigqmpKS0tLRw/fpyamhrWrl0rCz0iHrx/SHYv1dXV\nvPLKKygUChYuXMi6deuwsbEhPT1dzkqUxFF94a2srAytVivM/oGLFy/y7rvvsmDBAjZv3oy3tzfw\n2f2rX6oodYBNTEykqqoKc3Nzfv7zn+Ph4TG5g5hC6HtPHzhwgPfff5+6ujqMjY25fPkyqamptLe3\nY21tjZ2dHWq1GldXV1l46+/vZ+bMmdNWBPqySHN07Ngx9u3bR1hYGNu3b2fr1q04OTmRk5NDamoq\nzs7OdxTecnJy8PT0RKPRTOYwJg0hugm+EEnZbmhoYP/+/Rw8eJDz589z+fJl1Go1VlZW8qJ3N+Ft\nZGRkWrcV/7pcu3aNCxcuUFhYiKGhIS4uLpiamvL2229jbm7Orl272LZtG0uXLkWr1dLf309mZiYD\nAwO4u7tjZWWFvb09Go2GlpYWEhMTmTVrljht+/84OjoSFBRETEwMycnJeHp6EhwczMKFCwkJCcHC\nwoKysjIcHR156KGHZFNQSXgT4qVAIBAIpjKdnZ1kZWXh4OBAaGgoxsbGpKWlkZqaSk1NDWNjY9ja\n2sp+VVLGW3NzM4mJicTHx5OYmMjly5d54oknWLRokfzZIh68fygUCjo6Onj77bcxMDBg586drF69\nGq1Wi6enJ8XFxbS3tzNz5ky8vLzkg/LAwEAAMjMzCQkJQafTTfJIJg9JTDt//jxlZWV873vfGzcf\n0v2rfx+rVCoiIyMJCgpi0aJFPPLII9O62+v9QJrvM2fOsHfvXhYvXsyTTz7J1q1bmTlzJu3t7aSl\npWFoaEhISAgGBgbY2tri6upKe3s7CQkJDA8Pi+qrL0FFRQX//Oc/CQoK4oknnsDf3x9zc3OGhobI\nzMzEyMiILVu2YGFhIb/Hy8sLCwsLcnNz2bRpk+yZPd0Qopvgc5EEt8rKSl5//XWqq6tRq9WoVCoK\nCgooKyvDzMwMV1fXOwpvSUlJzJkzR5TdfU0sLCwIDAykoaGB5ORkjIyMsLKyIjY2lujoaBYuXCi/\nVjqt7OjoICMjA2dnZ3x8fICbXZMcHByIiopi5syZkzWcBxIHBwdZeMvOzsbNzQ0XFxfs7OwIDg5m\n5syZzJ8/Hx8fH7kETGwUBAKBQDAdsLGxoaenh4sXL7Jp0ya2bNmCp6enbL2QmppKeno6BgYGDA8P\nY29vL3u22dra0t3dTWBgIBs2bGDFihXA9PIEm0wqKys5ceIE0dHRREdHAzdN/T/99FPy8/N59tln\nWb58OXDTs0+KcYKCgggPDyc8PHzSvvuDgFTps3//fgB27tx5x9dJ93N/f/84b2XRpfT+0dbWxrvv\nvouzszNbt27Fy8sLgNraWhISElAqlfzbv/0bVlZWsnhqa2uLRqOhp6eHNWvWiKYJX4K8vDxSU1N5\n8sknZWuA9PR03nvvPYaGhtizZw+Ojo4MDQ3R09MjH8L4+vqyZs2aaV0VJEQ3wV2R0nbr6+t58803\nsbe3Z9u2bezatYslS5Zw+fJlioqKuHLlCubm5mi12nHC29DQEIWFhbi7u8uLoOCro1Kp8PLyoqGh\ngdTUVAYHB7ly5QqbN2/GysqK0dFRAPmBYmJiQnZ2NhUVFSxdulTurOng4CAbG4uAdzz6wtvFixfx\n8PDA2dkZuLnhUKlUouOuQCAQCKYVUqxgampKcnIyDQ0NLF68GJ1OR2RkJKGhoYyMjFBfX092djaZ\nmZm0t7djYmKCr68vgYGBLF++nDlz5shlR/r+QIL7S3p6Ovn5+ezYsQO1Ws3ly5c5duwYqamp7N69\nWxZBAc6dO4elpSVWVlYA2NrajmuWMRX5ovJmqaohKSmJ9vZ2HnroIUxMTMbNiX6p47Fjx7C2tpbn\nUHD/uHLlCkeOHGHDhg1EREQwOjpKVlYWH374IX19fezZswcHBweGh4fp7OyULY7UajVRUVGyL6Xg\n85HudanS7bvf/S7GxsZkZWWxb98+enp6eOONN+Qy9L6+Pj755BM0Go38OzAyMpqya8i9IJ52grui\nUCjo7u5m//79WFhYsGHDBhYsWADAvn37SEtLIzw8nK6uLg4ePEhycjJDQ0Py+7dt28Zrr70mn54J\nvj5arZannnoKPz8/4uLiaG9vp7m5GQClUimXPAJERUURGRlJV1cXPT09d1zoRMB7O4GBgbz88sv0\n9/fz1ltvkZubK/9NmluBQCAQCKYLUqzg5eVFUFAQ1dXVXLx4EbiZiR8QEMAPf/hDnJyc5KYKp0+f\n5vXXX+dXv/oVKSkpDA0NjXuGivhj4hkdHZUPYEdGRuR/l4SGkZER2tvbOXz4sCy4rVy5Un5dTEwM\nH3/8Me3t7fK/SbHjVL1e+sLZlStXSElJ4cCBAxw6dIi4uDi6u7vluXRzc6O7u5sLFy4AN+dEmnPp\nMz7++GPOnDlDd3f35AxoCnOnGFyaZ6miKjs7m48++kgWgRwcHOT3/ud//ieZmZnye4U1zN25da6l\n37+TkxOjo6O0trZSWlrK3r17bxPcAN577z2ysrLGfcZ0FtxAZLoJ7oD+w6Ouro4PP/yQ6OhoVq1a\nBcCnn37K4cOHWblyJRs3bkSj0ZCSksL169cxMTEZl/EmeYZN5ROybxop462jo4MrV67Q3t5OQECA\nXCOv34q5sLCQ6upqoqOjRYnvl+DWUlPJ+FPcwwKBQCCYjkiZaQ4ODqSkpAAwd+5cOd57++23ycvL\nY9WqVWzcuBF/f38aGxupra0lNDQUX19f8Qy9TzQ0NGBoaChXNJSXlxMTE4NOp8PExIS+vj4SExOp\nra2lvLycjIwMnnrqKTmuh5slqEePHsXW1pZly5Zhbm4+iSP6ZtDPtnz//ff5+OOPiYv7jjiaAAAg\nAElEQVSLo6SkhOLiYrKzs8nOzmZ0dBSNRoOzszMXLlyguLgYOzs7PD09x5nw5+TkcP78eRwcHIiO\njpZL6wRfH/1sxM7OTrlct7Ozk7i4OOzs7Ojr6+Ojjz6it7f3NhHo4MGDFBQUsGDBArmCRXBn9H8X\nXV1dDA0NYWxsDNwUOVNTUykuLiYrK4v+/n5ee+21cX6Fkn9nSEgI8+fPF13Z/z9CdBMAcOLECVJT\nUwkLCxuXRq5Wq+nu7mbjxo0YGRkRGxvLvn37WLhwIY8++qhs6B8bG0trays1NTWYmZnh4eExLrgS\ngdbEolKp0Gq1XL9+XW6uoNFosLCwkAOAhoYGTp06hZmZGStXrpRPOgX3hr7wlpycjJ+fnzDBFQgE\nAsG0RN8sPi8vj6KiIgICAnB0dOSPf/wjycnJLF++nA0bNuDp6YmXlxeLFy8mMjKSWbNmTfK3n7qk\np6fz+uuvY2lpyYwZM6itreXnP/85/f39REVFYWlpib29PRUVFZSVldHY2MiOHTtYu3atLGTU19dz\n/PhxysrK2Lx5M/7+/pM9rPuOfjnob37zG9LS0tDpdGzdupVFixYRHh7OjRs3qKmpobS0lNbWVubP\nn4+Xlxfp6enk5OQwMjKCqakpSqWSc+fOceLECTo6OnjxxRext7ef5BFOLaT159ChQyQkJDB37lzg\npvWLJCSXlJQwNDR0m+CWnp7OqVOn8PLyYs2aNbKAJLgdfcEtISGBQ4cOUVNTg4eHB2ZmZri4uFBT\nU0NFRQVDQ0P87Gc/G9dUJDMzk8OHDwPwzDPPYGNjMynjeBARots0Z2xsjLa2Nvbs2UNlZSUjIyME\nBwejUCgYHBzEwMCAsLAwjIyM6Onp4eOPP0ahUPDkk0/KrcVVKhV5eXmEhYVRVVVFeHi48HCbACTh\nc2BggP7+fq5fv46pqSkGBgYoFAqsrKzw8PCgqamJ5ORkWlpa5G6y5eXlnD59msLCQrZs2UJwcPBk\nD+dbiYODA35+fiQlJbFx40bhzyEQCASCaY2ZmRlmZmZkZmaiVqs5d+4c6enprFixgk2bNmFnZ8fY\n2Bijo6OYmJjI4oOoeLg/dHZ2UlRURGlpKe3t7bzzzjv4+vryxBNP4O3tDdwULMLCwigoKKCjowNz\nc3Pc3NwYGBigsLCQgwcPkpOTw9atW2V/ty/yOfu2I43tL3/5C9nZ2Tz22GNs27YNHx8fXF1d0el0\nLFiwAHNzc5qbmyktLaW7u5sVK1bg4eFBdnY2JSUlxMbGcuLECQoLC1GpVLz00kvy/kgwsXR1dbFv\n3z6KiooICQnB3t4epVLJyMgIlZWVdHR0sGPHjnGN4hITEzl06BD9/f08//zzcgWW4Hb0hegDBw7w\n8ccfMzY2xqJFi/Dz85PXhPDwcCoqKmhubqa+vh5jY2OuXbvGiRMnOHXqFL29vfzsZz/DxcVlkkf0\nYKEYEyZFAuDixYv8+te/BuDRRx9l27ZtwE3/B8k4vqWlheeff56HHnqIH/zgB7IaXlRUxJ49e/jx\nj3+Mp6enULUnAGluL1++zMGDB6mpqaG7uxt7e3uWLFnC7NmzcXR0ZHR0lMbGRj744APy8vKAm54T\n7e3tqFQqoqOjWbt2LTD1A6j7ycDAgCgTEAgEAoEAuHr1Knv27KGxsRGlUkl0dDSPPvqoLLiJWOOb\nY3h4mOvXr/Paa6/R1taGWq1m9+7dREREAMgeb0qlktbWVt566y3Ky8sBMDQ0lDvNPvroo7K/23Rp\ncpGXl8f//M//MHv2bHbs2IGlpaU8dmn/MzQ0REJCAocPH6a3t5fvf//7zJ8/X84crK2txczMDG9v\nb0JDQ4Ux/30mLi6Ov/zlL+P2qnBTJDp58iSjo6MEBQWh1Wqpra2lsrISExMTfvazn8lNXASfz5kz\nZ/jnP//JsmXLWLNmzbh5k34fAwMD/PnPfyYrK0v2PDQ3N8fPz4+dO3cKwe0OiCLbaY6kuUZERPDS\nSy/x5ptvcvToUeBmIwQDAwP5B2ZqaopKpaK1tRW4+QBvaGggJiYGe3t7nJ2dZcFtujyw7wfSSUNV\nVRWvv/46RkZG+Pn5YWVlRU1NDXv37qWgoIDvfve7uLu7o9Vq5S4ymZmZaDQaHn/8cXx9feWHv7ge\nXw8huAkEAoFAcBNHR0fCwsJobGzEx8eH9evXC8FtkjA0NGRwcJCOjg4UCgW9vb1cu3aNoaEhuVug\n1GTLzs6Ol19+mfT0dJqamujs7MTf3x83Nze5RGw6xYvl5eX09vaybt06LC0tx2X6GBgYMDY2hpGR\nEYsXL6a5uZnjx49z/Phx5s6di6+vL76+vpM8gumDtLbMmTOHCxcuEBcXx6pVq+TMtU2bNqHRaMjK\nypLLfx0dHZk7dy6PPvqosIe5R5qbmzl37hze3t6sX78eV1dX4LP5l5qHmJiY8MILL1BZWUlbWxu9\nvb34+PigVqunhR/kV0GIbtMc6UE8NjZGeHj4HYU36cTHyMiI4OBgUlNT+fWvf41Go6G0tJTq6mp2\n7dqFRqORP3e6PLDvBwqFgqtXr/LHP/4Re3t7tmzZQlRUFAC1tbW88cYb5Ofns27dOvn0QavV8vjj\nj9Pd3U1WVhYbNmyQBTf9IEIgEAgEAoHgqyJtvtatW0dOTg69vb3ygasQ3SaHlpYWFi1ahLu7Oxcu\nXODTTz9lZGSEZcuWYWpqKh+wj46OYmhoyMKFC+/4OdMlXhwdHWVwcFD2RLa2tr6j2CjtkYyNjXns\nscfIzc2lrq6O5uZmXFxc5JJp6XXi3p8YpGsh3bf6zSrMzc0JDQ3l4MGDnD59mq1btwI3RdKFCxey\ncOFCrl69ytjYGGq1GoVCIYz8vwRtbW1cuXKFJ598UhbcYLw3uyS8KZVKfHx8JuNrfiuZ+iur4AuR\nfkj6whvA0aNH+eijj4Cbi5m5uTmPP/448+fPp7S0lFOnTtHV1cX3vvc91qxZI3+G4KsjzV9+fj6t\nra2sWLFinOB2/Phxurq6+P73v09oaChws7RAoVCg1Wp56qmnePHFF8ctgiIIEAgEAoFAMBFIMYWF\nhQVarZaGhgaOHTsGiAPXyWLWrFls27aNtWvX8oMf/ACVSsXBgweJj4+nv79fvmZKpZIbN27Q29sL\n3B6zT5d4UareMTY2xtjYGAMDA1lIuBWFQsHIyIhcQjo8PMyNGzfkz9FvMCL4+ugLv/r3LiCXMa5f\nvx6NRkNRUZFcBqx/7RwdHXFycsLIyEi2SBJ8PtJaUFdXN+7/pTmXkOa5u7ubxsbGb/AbfvsRjRQE\nwPiHhbOzMz4+PiQnJ1NWVsbQ0BAhISEAWFlZMXPmTJYuXcqSJUtYtWqV/LfplJI+UVy5cgVDQ0OM\njIyAz67D8ePHaWtr41/+5V8wMjKirq6OI0eOkJKSwu7du4mOjgZuPpCqq6tRqVTyaZ1k4CpMiwUC\ngUAgENwPjIyMcHZ2JiYmhqGhIWbPni26An4DSLHd4OAgIyMj3LhxA1NTU9kGw9raGi8vL4qKisjN\nzZXFUSMjI5qamti/fz9VVVX4+/tP2wygsbExhoeHSU9Pp76+HltbW3x9fT83ZlYoFJSUlFBRUcHy\n5cuFIf99QroGH374IW+//bbcHdbW1lbeYyqVSjo7O0lPT8fCwgI/P7+7XjuxD7o3pHkaGBggISEB\ntVpNVFSULEZL2ZzSNfjb3/5GS0sLfn5+Qti8R4ToJpC5V+FNqVRiaWmJjY2NXLc9XVLSJ5ITJ07w\nj3/8gxkzZmBvbz9u/jMzM7l27Rpr166lra2NTz/9lLS0NHbv3i0b3QIcOXKEQ4cOMW/ePMzMzMZ9\nvnjQCAQCgUAguF+YmZmRn5/PwoULCQoKmuyvM+WRDrfr6ur46KOPOHbsGBcuXKCyshIAtVqNiYkJ\nNjY2svB28eJFFAoFAwMDxMTEEBcXR1hYmBzTT0cUCgUGBgYYGBiQlZXF6OgoXl5eqFSq216rv785\nfvw4AI8//rgQGu4zMTExtLe3k56eTkZGBj09PahUKiwtLTE0NMTCwoKkpCS6uroIDw+/bQ8kuDuf\nVwo9MjJCZmYmly5dws7ODk9PTxQKBcPDw/I9n5qaysmTJ/Hx8SEoKEjs/+8RIboJxnEvwpu+6n2n\n9wm+mDNnzvDee+8RHBzMnDlzsLKyAj5bCCsqKigoKGBgYIDMzEzS09NvE9zKyso4evQozs7OzJ07\nV5wwCwQCgUAg+MYwNDRk7ty5suAmfK3uLwqFgqqqKl599VVqamowMzOjq6uLyspK8vPz6ejoYMaM\nGZiammJjY4OPjw9lZWWkpKSQnp7O5cuX2b59Oxs2bJjsoTwQqFQqCgsLKSsrw9DQEHd3d1m8GR0d\nHSe4JSYmEhMTw6xZs4iIiBjnMyaYOEZGRlAqlcybN4+IiAicnJyoqKggPz+f9PR0ampqcHFxQafT\noVQqSUpKIjAwUHTLvEf0q9KGhobo6+ujr68PU1NT4GZFm0qlIisri+LiYiwsLPD29pbfk5GRwaFD\nhzA0NGTXrl3y/lXwxQjRTXAbnye8jYyMEBwcjEKhEOWLXxGpFfPChQvZvHnzuAeFNJ/Ozs4kJydz\n6dIl6uvr2bVrF6tXr5bnXPJQaWho4LHHHsPT03OyhiMQCAQCgWCaIh34CcHt/tPd3c1bb72Fra0t\nTz75JE8//TTz5s3Dy8uL2tpa8vPz6e7uJjAwEBMTE+zs7Jg7dy5DQ0MEBQWxZs0ali1bBggLEgBT\nU1N8fX2Jj4+nrKyMwcFBLC0tsbOzGyeqZWZmcuTIERQKBc8++yxWVlbTfu4milvvQ/2sKZVKxYwZ\nM4iIiGDGjBnU1dVRXFxMUlKSnN3Z0tJCbW0ts2bNkoUjwZ3RF9zi4uI4ePAgn376KbGxsVy5cgVj\nY2McHBzw8PDAwsKCnJwccnNzKS8vp6ioiJiYGM6ePcvg4CAvvfSSEDq/JEJ0E9yRuwlvpaWlmJub\nM2PGDPHA+QqcPn2ad999lwULFrBx40bZfw2gt7dX9nazsLDA3NyciooKjIyMmDdvHhqNBkNDQwoK\nCjh48CBZWVk88cQTLF26FBABr0AgEAgEgslBxB/3B0mU6O7uBuDcuXOsXr1a7kBqYWGBu7s7gYGB\nXLp0iZKSEhwcHPD09GR0dBRTU1PCwsIIDg6WuxEKD+bPsLGxISwsjNTUVMrKyqioqKCpqYmRkRGa\nm5s5duwYZ86cobe3l5///OdCaJhA9O/D0tJSCgoKuHDhAjdu3KC/vx97e3vgpvjm7u7OkiVL0Gg0\njI2NkZWVRX19Pf39/VhaWrJs2TLZ11BwO/pZmwcOHOCDDz5geHgYHx8fTE1NycvLo6ysjL6+Pvz8\n/JgxYwY6nY5r167R0NBAeXk5IyMjBAYG8txzz43bvwruDcWYaDcp+Bz02zVnZGTwhz/8ARcXF37x\ni1/Ii6Hg3oiJieGdd95h+fLlrF27dtyC1drayvnz5xkaGmLHjh0AdHV1kZyczIEDB+jp6UGj0WBi\nYkJjYyPGxsZs2rSJtWvXAiKAEggEAoFAIJiK1NTU8OqrrxIQEEBlZSV/+MMfsLS0vC32y8jI4M9/\n/jMBAQG89NJLk/iNv300NDTw0UcfcfHixXFdXU1MTAgMDGTnzp1CcJtA9O/dI0eOcPz4cXp7e+Xu\nmAYGBjzxxBM88sgjAAwPD49r/JGenk5hYSF5eXn89Kc/xd3d/ZsfxLeQ+Ph4/vrXv7JkyRJWr16N\nh4cHfX19nDx5kv379+Pv789Pf/pT2bO9o6ODwcFBrl27hqurK6ampiKj8CsyPdvWCO4ZqVvJyMgI\nc+bMYdGiRSQlJdHa2ipEty9Be3s777zzDgBarfY2we3kyZOcPHlynM+GSqVi5cqVhISEcPjwYdrb\n2+nt7WX9+vUEBwcTHBwMCMFNIBAIBAKBYKoyMjJCb28vRUVFmJmZ0d7ejqWl5W2v8/X1Ra1WU1xc\nTFNTE87OzpPwbb+daLVa/uM//oO6ujqKiooYHR3F2NiY0NBQ7OzsZBFCMDFI+5ajR4+yb98+Zs2a\nxZIlS1AqldTU1LB//3727t1Ld3c327Ztw9DQUM76VCgUzJ07l6ioKIaHh4UIdA+MjY0xMDBAUlIS\nGo2GNWvWoNPpACgsLCQlJQWVSsVzzz2Hubm5LHLa2NgA4OjoOJlff0ogRDfBFyJ1+YGbJw1KpVKk\n8H5JbG1t+dWvfsVrr73Ge++9h62tLfPmzeP69eucPHmSU6dOsW7dOrZu3Qp8JqQZGBjg5ubGc889\nJ/vo6QtsQnATCAQCgUAgmJqMjo7i4+PD66+/ziuvvEJHRwfnzp1j9+7d4xqbKRQK1Go1rq6udHV1\nieZaXwFjY2N8fHzw8fGZ7K8yLaiqquL48eNERkayfft2WSSOjIzE09OTf/zjHxw9ehS1Ws3q1atv\n2+8YGhqOy34T3B2FQkFPTw8lJSVER0ej0+kYHR0lOzubjz76iJ6eHt544w05oaa1tVXuhCyYGMRu\nXXDPZGVlUVJSgre3t8hy+woEBwcjWSi+9dZbnDt3jtjYWE6dOsXatWvZuXMnMF5IkzIN9dH/fyG4\nCQQCgUAgEEwdpBI7Kd4bHR3F19eXV199FWNjY86dO8exY8eAm3Gg5Kd3+fJlqqqq0Gg0skew4Msj\nnJcmBuk+hjvPaVNTEzdu3GDx4sU4OzszNjYmvycyMpInn3wSgMOHD9PQ0PDNfOkpjLSnlITKixcv\njhPc9LPZfvOb33Du3LnJ+qpTErFjF9wzarWazs5OfvCDH9wxrV3wxQQEBMjC29///ncOHjzIY489\nJj9Y7pS5pt9BST+4EggEAoFAIBBMDSTBQfpvT08PSqVSjgu9vLx4+eWXMTIyYu/evbz33nt0dXUB\nUFlZyenTp2lra2P58uWoVKrJGcQUQMTZXx994/62tja5Wkf6G0BzczPw2f2uUChQKpXy32fPns2K\nFSvo7u6WG4kIbudeROKRkREArKysyM/P59SpU3zwwQf09PTw+uuvjxPcjh07JttICQF64hA5mYJ7\nxtvbm/fee0/Uzn9NAgICePnll3nllVcA8PPzA24uiCJzTSAQCAQCgWB6IR261tfXc+LECaqrq+ns\n7MTf35+wsDCWLVsGgI+PD//1X//FK6+8wqlTp8jJycHAwIChoSGGh4fZuXOn/FrR1V4wWUj33Suv\nvEJJSQn//d//jYuLy7jkAqkxRUVFBXPnzpWTDBQKBSMjIxgYGKDRaBgeHqahoQF/f/9JG8+Div58\ntra2cunSJcrKyujq6iIwMBAXFxdCQkIwMDBArVazatUq9u/fzyeffIKxsTFvvvkmdnZ28udlZ2cT\nFxeHTqcjIiJCrB8TiMF/SWk3AsE9IGrnJwYHBweCgoJISEggKSkJV1dXdDqdnPorFjmBQCAQCASC\nqY+0ca6srOS1116jubkZBwcHXFxcKCgoICMjg87OTiIiIoCblSdhYWEkJSXR1dWFm5sbzzzzDCtW\nrCAyMnLcZwoEk0lubi4NDQ1kZGQQERGBtbW1nGRgaGhIQUEBRUVFzJgxAycnJ2B8llxaWhqNjY08\n8sgjqNXqyRzKA4f+b/zEiRN8/PHHnD17lqqqKhoaGsjLyyMxMRGFQoGHhwdGRkbY2NjQ2tpKXV0d\nUVFRhISEyE1Czp49y+HDh+nu7ubHP/4xDg4Okzm8KYcQ3QSCScLBwYHg4GDi4+PJyMhAq9Xi5uYm\nhDeBQCAQCASCaYJCoaC5uZlf//rX2NrasmPHDrZv3878+fNxdXUlNzeX8vJyQkNDsbW1ZWxsDLVa\nzcyZM0lMTKStrQ2dTkdYWBhws+mZ1ABNIJgMpAYf8+bNo6Ojg5KSEtLS0oiMjMTa2hq4WerY399P\nfn4+GRkZ6HQ6bG1tZT/C3NxcTp48iUajYeXKlaKJnx76gtvbb79NXFwchoaGbNmyhejoaPz8/FCr\n1dTU1FBSUkJ3dzc+Pj44ODhgZWVFT08PmZmZJCUlUVBQwPHjx0lNTcXU1JSf/exnaLXaSR7h1EOI\nbgLBJKIvvKWnp+Pm5oZWqxWCm0AgEAgEAsEURxInYmNjycrKYvPmzSxatAiAmpoaEhISqKqq4tln\nnyUqKmqcz68kvMXHx5OXl4eZmRm+vr7jupoKBJOB5OGmUCiIjIykvb2d0tJSWXiTPAf9/f0ZHByk\npKSErKws6uvruX79OklJSZw+fZre3l6RdXUL+oLbm2++SX5+PsuXL+fpp58mJCQEZ2dnfHx8iIyM\nxNnZmZycHKqqqhgYGCAiIgInJyc5s7ClpYX29nasra1ZunQpO3fulLvICiYWIboJBJPMrcKbRqNB\np9NN9tcSCAQCgUAgEEwgQ0NDchaafhnd0aNH6e3t5fnnnwegrq6OI0eOkJKSwu7du1mxYgUAXV1d\n1NTUyCbndnZ2svBWUlKCQqHA399fCG6Cb4w7CbzSvT08PIxSqfxc4W3mzJlYWFjQ29tLfn4+hYWF\nNDY24urqyo9+9CORdaWHvuD2xhtvUFxczObNm1mzZg02NjbjOsYqFArc3d3RarWkpaVRVVWFhYUF\nvr6+8n+XLl3KmjVrWL58OQEBAXKpqWDiEaKbQPAAoO/x5uvrKzdXEAgEAoFAIBB8+6mpqSE+Pp66\nujp8fHxkO5GRkRHOnz/P8PAwa9asoba2lsOHD5OWlsbu3btZuXKl/BknT54kNjaWsLAwzMzMGB0d\nxd7entDQUM6dO0ddXR3Lli3D2Nh4EkcqmC7oi0CxsbFYWVkxPDwsN927V+HN19eXWbNmERkZSWRk\nJOvXryc6Ohp7e/tJG9uDiCRu/va3vyU3N5fvf//7LF68GAsLC1nolLJhJasirVaLWq0mJyeHlpYW\nwsPDMTc3l1+nfwggxPr7h3DFFwgeEAIDA/m///s/YRQqEAgEAoFAMIVISUnhwIEDNDY2snz5ctra\n/l97dxtTdf3/cfzFORwuPFyjIIKSUZjoDC8mmCZuzjJkEim6XE1vueVsurIbpm6n0tpKu9EqbW6u\nuWaptLnUMpyKBXJAbKYnECkhryCUf4oeETic87/BOEEC2s+vHvU8H7fknO/F+3tk7uzl+/P+/J/3\n+15gYKCGDx+uffv26eDBg6qqquo1cDt16pT279+vUaNG9Qg13G63nnjiCX3wwQcKCgpSWFiYT54R\n/qcrcFu/fr2OHj2q2NhYBQcHa8qUKUpMTNS4ceN6bMK3ePFieTweHTx4UDabTTabTYmJiXK73QoL\nC2OH0jtw7tw5VVRUSJLMZrMGDBjg7XDrHpp1D94yMjJ06NAh1dbWyul0ev/eum+2QuB2b9HpBjxA\nQkNDJfXeqg0AAICHS2FhobZs2aK4uDgtWLBAeXl53u97Xd/1WltbZbfb5XA4dObMGS1ZskTTp0/3\nXuP8+fP69ttvdfnyZeXl5Wno0KHe97rmZ8XExHg7h4D75dy5c/ryyy8ldf4uhoaGym63y263q7S0\nVA6HQyaTSS0tLYqNjdWECRPU0tIih8OhI0eO3LKrKfoXGRnpXR119OhRJSYmatiwYb1uxNf156Cg\nIFVWVqqurk7jx4/XkCFDfFW+3yJ0Ax5ABG4AAAAPt+LiYm3atEkTJkxQfn6+xo4dK0m3dKYkJSXJ\n6XTq1KlTioqK0ksvvaTw8HBJUmVlpb777juVl5drwYIF3o0WuuN7I3ylewhksViUm5ur+fPny2w2\n6+rVq6qqqpLdbldRUZF+++03NTQ0KCMjQ42NjTp//rzKy8uVnp6uqKgoXz/KQ6P7PPCysjIlJSVp\n6NChvQZvXXMky8vLdfbsWU2bNk3x8fE+rN4/BXg8Ho+viwAAAACAR8WFCxe0fv16mc1mvfbaa0pJ\nSZEkuVyuHkvuGhsbFRcXJ5fLpW+++Ua7d++WJI0YMUJut1t1dXUym83Kz89XTk6OpJ6ztIAHgcPh\n0Hvvvafg4GCtXr1aqampcrvdOnPmjGpqavTrr7/q9OnTcjqdGjBggEwmk9ra2tTW1qbBgwdrw4YN\nMpvNBMj/QVVVlbr6p5YvX65JkyZJ+mc+W/d/J1asWKHg4GC9++673jluuH/odAMAAAAAA9XU1Gjf\nvn2aPXu2MjMzJXWGZWazWS6XS4WFhdqzZ4927NihEydOyOVyafbs2RoyZIg6OjrU1NQkj8ejzMxM\n5eXlaerUqd5rELjhQRMXF6dRo0bpwIEDKi0t1dChQ5WYmKiYmBg9+eSTmjx5srKyspSamqqgoCC1\ntbXp8uXLCgsL08qVKxUdHU3g9h9173iz2+09Ot46Ojq84dquXbtUVlam559/3rtZH5/1/UWnGwAA\nAAAYoKvL5KuvvtLu3bu1cOFCZWdnS+oMzBobG/XFF1+osrJSZrNZHo9HbrdbISEhevHFF5WXlyeX\ny6X29naZTCYFBwd7r03ghgddZWWl3nnnHYWEhGj58uXeJdX/7vC8fv26GhsbZbVaWe54l/rqeJOk\niooKbdu2TeHh4Vq+fLmio6N9VKV/o9MNAAAAAAzQ1UFy8+ZN2e12SVJsbKxaW1tVUlKizZs36+zZ\ns0pLS9OyZcs0ffp0JScnq7q6WvX19Zo4caKsVqssFosCAwN7zGiiOwUPukGDBnk73ioqKvTYY48p\nISFBJpNJHo/H+/tssVgUExPDbrsG+HfHW3JyshITE3X8+HFt375dzc3NevPNNxUXF+frUv0WoRsA\nAAAAGCg8PFx1dXU6efKkysrKvLsNxsbGKjc3V4sWLdKgQYMUExOj+Ph4VVdX6/fff9dTTz2lxMRE\n73UI2vCw6St46x4e83ttrO7BW2lpqTwejw4ePKiLFy/KZrP12PEY9x+hGwAAAAAYKDg4WGlpafJ4\nPKqvr1doaKimTZuml19+WePGjZPFYpHL5fIuIXU4HGpsbNTs2bMVGRnp6/KBu/Lv4G348OEaPHgw\nYds91D14q6qqUltbm2w2m5KTk31dmt9jphsAAAAA3AMej0fXrl2TJEVERHhf7xqX0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"text/plain": [
"<matplotlib.figure.Figure at 0x10be2d4e0>"
]
},
"metadata": {
"image/png": {
"height": 372,
"width": 622
}
},
"output_type": "display_data"
}
],
"source": [
"# opens the file `odisha-tomato-cuttack-banki.csv` (which needs to be in the same directory as this notebook)\n",
"# in read-only mode (specified using the string 'r')\n",
"f = open('odisha-tomato-cuttack-banki.csv', 'r')\n",
"\n",
"# Python has a package `csv` that helps us work with csv files.\n",
"import csv\n",
"reader = csv.reader(f)\n",
"\n",
"line_number = 0 # keep track of which line number we are at, starting from 0\n",
"modal_prices = [] # we build up a list of modal prices, starting from an empty list\n",
"timestamps = [] # we similarly build up a list of datetime objects, starting from an empty list\n",
"\n",
"prices_by_month = [[] for month in range(1, 13)]\n",
"for line in reader: # go through each line of the csv file\n",
" if line_number >= 2: # note that we ignore the first two lines because they correspond to headers\n",
" date = datetime.strptime(line[-1], '%d-%b-%y')\n",
" price = float(line[-2])\n",
" prices_by_month[date.month - 1].append(price) # date.month ranges from 1 to 12, whereas we\n",
" # for the list, we want it from 0 to 11, so\n",
" # subtract 1\n",
" line_number = line_number + 1\n",
" \n",
"def average(list_of_values):\n",
" return sum(list_of_values) / len(list_of_values)\n",
"\n",
"month_names = []\n",
"average_prices = []\n",
"for month in range(1, 13):\n",
" # create a new datetime object and pull its full month name\n",
" month_names.append(datetime(2018, month, 1).strftime('%B'))\n",
" average_prices.append(average(prices_by_month[month - 1]))\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"xcoords = range(1, 13) # plot the months evenly spaced at x coordinates 1, 2, ..., 12\n",
"plt.plot(xcoords, average_prices, 'o')\n",
"plt.xticks(xcoords, month_names, rotation=45) # label each x coordinate with a month name\n",
"plt.xlabel('Month')\n",
"plt.ylabel('Average INR/100 kg')\n",
"plt.title('Tomato Prices Over Time')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Min,Max,Modal Price from Orissa,Cuttack[Tomato],Banki from 01-Jan-2011 To 31-Dec-2016 (Total-536) (Total-536)
State Name Market Name Group Variety Grade Min Price (Rs/Quintal) Max Price (Rs/Quintal) Modal Price (Rs/Quintal) Price Date
Orissa Banki Vegetables Other FAQ 1500 1800 1700 3-Apr-12
Orissa Banki Vegetables Other FAQ 1500 1800 1600 5-Apr-12
Orissa Banki Vegetables Other FAQ 1300 1500 1400 7-Apr-12
Orissa Banki Vegetables Other FAQ 1200 1500 1300 10-Apr-12
Orissa Banki Vegetables Other FAQ 1400 1600 1500 14-Apr-12
Orissa Banki Vegetables Other FAQ 1500 1800 1600 17-Apr-12
Orissa Banki Vegetables Other FAQ 1400 1600 1500 21-Apr-12
Orissa Banki Vegetables Other FAQ 1500 1800 1600 24-Apr-12
Orissa Banki Vegetables Other FAQ 1300 1500 1400 28-Apr-12
Orissa Banki Vegetables Other FAQ 1300 1500 1400 5-May-12
Orissa Banki Vegetables Other FAQ 1300 1500 1400 8-May-12
Orissa Banki Vegetables Other FAQ 1500 1800 1700 12-May-12
Orissa Banki Vegetables Other FAQ 1500 1700 1600 19-May-12
Orissa Banki Vegetables Other FAQ 1500 1800 1600 2-Jun-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 5-Jun-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 9-Jun-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 12-Jun-12
Orissa Banki Vegetables Other FAQ 1500 1600 1550 16-Jun-12
Orissa Banki Vegetables Other FAQ 2500 3000 2800 19-Jun-12
Orissa Banki Vegetables Other FAQ 1500 1700 1600 3-Jul-12
Orissa Banki Vegetables Other FAQ 1700 1800 1750 7-Jul-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 10-Jul-12
Orissa Banki Vegetables Other FAQ 1500 1800 1600 13-Jul-12
Orissa Banki Vegetables Other FAQ 1500 1700 1600 14-Jul-12
Orissa Banki Vegetables Other FAQ 1300 1500 1400 17-Jul-12
Orissa Banki Vegetables Other FAQ 1500 1700 1600 21-Jul-12
Orissa Banki Vegetables Other FAQ 2500 2800 2600 24-Jul-12
Orissa Banki Vegetables Other FAQ 2400 2800 2500 28-Jul-12
Orissa Banki Vegetables Other FAQ 2600 3000 2800 31-Jul-12
Orissa Banki Vegetables Other FAQ 1500 1800 1700 4-Aug-12
Orissa Banki Vegetables Other FAQ 2500 2700 2600 7-Aug-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 11-Aug-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 14-Aug-12
Orissa Banki Vegetables Other FAQ 2500 2800 2600 18-Aug-12
Orissa Banki Vegetables Other FAQ 2400 2800 2600 21-Aug-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 1-Sep-12
Orissa Banki Vegetables Other FAQ 2500 2800 2600 4-Sep-12
Orissa Banki Vegetables Other FAQ 2000 2200 2100 8-Sep-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 11-Sep-12
Orissa Banki Vegetables Other FAQ 1500 1600 1550 15-Sep-12
Orissa Banki Vegetables Other FAQ 1800 2000 1900 18-Sep-12
Orissa Banki Vegetables Other FAQ 1500 1800 1600 3-Nov-12
Orissa Banki Vegetables Other FAQ 2000 2200 2100 6-Nov-12
Orissa Banki Vegetables Other FAQ 800 1000 900 8-Nov-12
Orissa Banki Vegetables Other FAQ 800 1000 900 1-Jan-13
Orissa Banki Vegetables Other FAQ 800 1000 900 2-Jan-13
Orissa Banki Vegetables Other FAQ 800 1000 900 3-Jan-13
Orissa Banki Vegetables Other FAQ 800 1000 900 5-Jan-13
Orissa Banki Vegetables Other FAQ 800 1000 900 7-Jan-13
Orissa Banki Vegetables Other FAQ 800 1000 900 8-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 10-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 11-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 13-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 14-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 15-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 16-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 18-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 19-Jan-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 20-Jan-13
Orissa Banki Vegetables Other FAQ 800 1000 900 1-Feb-13
Orissa Banki Vegetables Other FAQ 800 1000 900 2-Feb-13
Orissa Banki Vegetables Other FAQ 800 1000 900 3-Feb-13
Orissa Banki Vegetables Other FAQ 800 1000 900 5-Feb-13
Orissa Banki Vegetables Other FAQ 800 1000 900 7-Feb-13
Orissa Banki Vegetables Other FAQ 800 1000 900 8-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 10-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 11-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 13-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 14-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 15-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 16-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 18-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 19-Feb-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 20-Feb-13
Orissa Banki Vegetables Other FAQ 800 1000 900 2-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 3-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 4-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 5-Mar-13
Orissa Banki Vegetables Other FAQ 800 900 850 7-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 9-Mar-13
Orissa Banki Vegetables Other FAQ 700 800 750 10-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 12-Mar-13
Orissa Banki Vegetables Other FAQ 700 800 750 13-Mar-13
Orissa Banki Vegetables Other FAQ 700 800 750 14-Mar-13
Orissa Banki Vegetables Other FAQ 700 800 750 15-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 16-Mar-13
Orissa Banki Vegetables Other FAQ 700 800 750 17-Mar-13
Orissa Banki Vegetables Other FAQ 900 1000 950 18-Mar-13
Orissa Banki Vegetables Other FAQ 700 800 750 19-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 20-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 22-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 23-Mar-13
Orissa Banki Vegetables Other FAQ 700 800 750 24-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 25-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 26-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 27-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 28-Mar-13
Orissa Banki Vegetables Other FAQ 800 1000 900 29-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 30-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 31-Mar-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 1-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 2-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 4-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 5-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 6-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 7-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 8-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 9-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 14-Apr-13
Orissa Banki Vegetables Other FAQ 1200 1400 1300 16-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 17-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 18-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 19-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 20-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 21-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 23-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 26-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 27-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 29-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 30-Apr-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 4-May-13
Orissa Banki Vegetables Other FAQ 1000 1200 1100 7-May-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 11-May-13
Orissa Banki Vegetables Other FAQ 1600 1800 1700 14-May-13
Orissa Banki Vegetables Other FAQ 2000 2400 2200 17-May-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 18-May-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 21-May-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 25-May-13
Orissa Banki Vegetables Other FAQ 2000 2400 2200 28-May-13
Orissa Banki Vegetables Other FAQ 2200 2500 2300 1-Jun-13
Orissa Banki Vegetables Other FAQ 2200 2400 2300 4-Jun-13
Orissa Banki Vegetables Other FAQ 3000 3500 3200 7-Jun-13
Orissa Banki Vegetables Other FAQ 2800 3200 3000 8-Jun-13
Orissa Banki Vegetables Other FAQ 3000 3500 3200 9-Jun-13
Orissa Banki Vegetables Other FAQ 3500 4000 3800 11-Jun-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 15-Jun-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 18-Jun-13
Orissa Banki Vegetables Other FAQ 3000 3500 3200 22-Jun-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 25-Jun-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 28-Jun-13
Orissa Banki Vegetables Other FAQ 3500 4000 3800 29-Jun-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 2-Jul-13
Orissa Banki Vegetables Other FAQ 3500 4000 3800 6-Jul-13
Orissa Banki Vegetables Other FAQ 3500 4000 3800 9-Jul-13
Orissa Banki Vegetables Other FAQ 3500 4000 3800 13-Jul-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 16-Jul-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 20-Jul-13
Orissa Banki Vegetables Other FAQ 4000 4500 4200 23-Jul-13
Orissa Banki Vegetables Other FAQ 3000 3200 3000 27-Jul-13
Orissa Banki Vegetables Other FAQ 3200 3500 3300 30-Jul-13
Orissa Banki Vegetables Other FAQ 3000 3500 3200 3-Aug-13
Orissa Banki Vegetables Other FAQ 3000 3500 3200 6-Aug-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 10-Aug-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 13-Aug-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 17-Aug-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 20-Aug-13
Orissa Banki Vegetables Other FAQ 2200 2500 2300 24-Aug-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 27-Aug-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 31-Aug-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 3-Sep-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 7-Sep-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 10-Sep-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 14-Sep-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 17-Sep-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 21-Sep-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 24-Sep-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 28-Sep-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 1-Oct-13
Orissa Banki Vegetables Other FAQ 1800 2000 1900 3-Oct-13
Orissa Banki Vegetables Other FAQ 2800 3000 2900 7-Oct-13
Orissa Banki Vegetables Other FAQ 3500 4000 3800 23-Nov-13
Orissa Banki Vegetables Other FAQ 2000 2500 2200 1-Feb-14
Orissa Banki Vegetables Other FAQ 2000 2200 2100 4-Feb-14
Orissa Banki Vegetables Other FAQ 1500 1700 1600 7-Feb-14
Orissa Banki Vegetables Other FAQ 1500 1700 1600 8-Feb-14
Orissa Banki Vegetables Other FAQ 1700 1800 1750 11-Feb-14
Orissa Banki Vegetables Other FAQ 2000 2200 2100 15-Feb-14
Orissa Banki Vegetables Other FAQ 1300 1500 1400 18-Feb-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 22-Feb-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 25-Feb-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 1-Mar-14
Orissa Banki Vegetables Other FAQ 800 1000 900 2-Mar-14
Orissa Banki Vegetables Other FAQ 800 1000 900 3-Mar-14
Orissa Banki Vegetables Other FAQ 1200 1400 1300 4-Mar-14
Orissa Banki Vegetables Other FAQ 800 1000 900 8-Mar-14
Orissa Banki Vegetables Other FAQ 800 1000 900 11-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 15-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 18-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 22-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 23-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 24-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 26-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 29-Mar-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 1-Apr-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 5-Apr-14
Orissa Banki Vegetables Other FAQ 1200 1300 1250 6-Apr-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 7-Apr-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 8-Apr-14
Orissa Banki Vegetables Other FAQ 1500 1700 1600 12-Apr-14
Orissa Banki Vegetables Other FAQ 1000 1200 1100 15-Apr-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 19-Apr-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 22-Apr-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 26-Apr-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 29-Apr-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 3-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 6-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 10-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 13-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 17-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 20-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 24-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 27-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 31-May-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 3-Jun-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 7-Jun-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 10-Jun-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 14-Jun-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 17-Jun-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 21-Jun-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 24-Jun-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 28-Jun-14
Orissa Banki Vegetables Other FAQ 3500 4000 3800 1-Jul-14
Orissa Banki Vegetables Other FAQ 3500 4000 3800 5-Jul-14
Orissa Banki Vegetables Other FAQ 3500 4000 3800 8-Jul-14
Orissa Banki Vegetables Other FAQ 3500 4000 3800 12-Jul-14
Orissa Banki Vegetables Other FAQ 3800 4000 3900 15-Jul-14
Orissa Banki Vegetables Other FAQ 3500 4000 3800 30-Aug-14
Orissa Banki Vegetables Other FAQ 3800 4000 3900 6-Sep-14
Orissa Banki Vegetables Other FAQ 3800 4000 3900 9-Sep-14
Orissa Banki Vegetables Other FAQ 5000 5500 5200 13-Sep-14
Orissa Banki Vegetables Other FAQ 5000 5400 5200 16-Sep-14
Orissa Banki Vegetables Other FAQ 4000 4400 4200 20-Sep-14
Orissa Banki Vegetables Other FAQ 3500 4000 3800 30-Sep-14
Orissa Banki Vegetables Other FAQ 2800 3000 2900 4-Oct-14
Orissa Banki Vegetables Other FAQ 3500 4000 3800 7-Oct-14
Orissa Banki Vegetables Other FAQ 4500 4800 4600 11-Oct-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 2-Nov-14
Orissa Banki Vegetables Other FAQ 2500 3000 2800 8-Nov-14
Orissa Banki Vegetables Other FAQ 2000 2200 2100 11-Nov-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 13-Nov-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 15-Nov-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 12-Dec-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 13-Dec-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 23-Dec-14
Orissa Banki Vegetables Other FAQ 2000 2400 2200 24-Dec-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 25-Dec-14
Orissa Banki Vegetables Other FAQ 1800 2000 1900 30-Dec-14
Orissa Banki Vegetables Other FAQ 1600 1800 1700 3-Jan-15
Orissa Banki Vegetables Other FAQ 900 1100 1000 6-Jan-15
Orissa Banki Vegetables Other FAQ 900 1100 1000 7-Jan-15
Orissa Banki Vegetables Other FAQ 1200 1400 1300 9-Jan-15
Orissa Banki Vegetables Other FAQ 1000 1400 1200 10-Jan-15
Orissa Banki Vegetables Other FAQ 1000 1400 1200 12-Jan-15
Orissa Banki Vegetables Other FAQ 1300 1500 1400 14-Jan-15
Orissa Banki Vegetables Other FAQ 1000 1400 1200 15-Jan-15
Orissa Banki Vegetables Other FAQ 800 1000 900 17-Jan-15
Orissa Banki Vegetables Other FAQ 1400 1600 1500 18-Jan-15
Orissa Banki Vegetables Other FAQ 1400 1600 1500 19-Jan-15
Orissa Banki Vegetables Other FAQ 1400 1600 1500 22-Jan-15
Orissa Banki Vegetables Other FAQ 1500 1800 1700 23-Jan-15
Orissa Banki Vegetables Other FAQ 1500 1800 1600 24-Jan-15
Orissa Banki Vegetables Other FAQ 1500 1800 1600 26-Jan-15
Orissa Banki Vegetables Other FAQ 1600 1800 1700 28-Jan-15
Orissa Banki Vegetables Other FAQ 1600 1800 1700 29-Jan-15
Orissa Banki Vegetables Other FAQ 1400 1600 1500 31-Jan-15
Orissa Banki Vegetables Other FAQ 1000 1400 1200 1-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1300 1200 2-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1400 1200 3-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1200 1100 4-Feb-15
Orissa Banki Vegetables Other FAQ 1100 1300 1200 5-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1300 1200 6-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1200 1100 7-Feb-15
Orissa Banki Vegetables Other FAQ 900 1200 1000 8-Feb-15
Orissa Banki Vegetables Other FAQ 800 1000 900 10-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1200 1100 11-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1200 1100 12-Feb-15
Orissa Banki Vegetables Other FAQ 800 1000 900 13-Feb-15
Orissa Banki Vegetables Other FAQ 800 1000 900 14-Feb-15
Orissa Banki Vegetables Other FAQ 1000 1200 1100 15-Feb-15
Orissa Banki Vegetables Other FAQ 900 1100 1000 16-Feb-15
Orissa Banki Vegetables Other FAQ 900 1100 1000 17-Feb-15
Orissa Banki Vegetables Other FAQ 800 1000 900 18-Feb-15
Orissa Banki Vegetables Other FAQ 800 1000 900 20-Feb-15
Orissa Banki Vegetables Other FAQ 900 1200 1000 21-Feb-15
Orissa Banki Vegetables Other FAQ 800 1000 900 22-Feb-15
Orissa Banki Vegetables Other FAQ 800 1000 900 23-Feb-15
Orissa Banki Vegetables Other FAQ 900 1200 1000 24-Feb-15
Orissa Banki Vegetables Other FAQ 900 1200 1000 25-Feb-15
Orissa Banki Vegetables Other FAQ 900 1200 1000 26-Feb-15
Orissa Banki Vegetables Other FAQ 1200 1500 1300 27-Feb-15
Orissa Banki Vegetables Other FAQ 1200 1500 1400 28-Feb-15
Orissa Banki Vegetables Other FAQ 1200 1500 1400 1-Mar-15
Orissa Banki Vegetables Other FAQ 1200 1400 1300 2-Mar-15
Orissa Banki Vegetables Other FAQ 1300 1500 1400 3-Mar-15
Orissa Banki Vegetables Other FAQ 1500 1800 1600 5-Mar-15
Orissa Banki Vegetables Other FAQ 1700 2000 1800 6-Mar-15
Orissa Banki Vegetables Other FAQ 1600 1800 1700 7-Mar-15
Orissa Banki Vegetables Other FAQ 1600 1800 1700 8-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 9-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 10-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 12-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 13-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 14-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 16-Mar-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 18-Mar-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 19-Mar-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 20-Mar-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 21-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 24-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 26-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 27-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 28-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 29-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 30-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 31-Mar-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 1-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 2-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 3-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 4-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 5-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 6-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 7-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 8-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 9-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 11-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 12-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 14-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 15-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 16-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 17-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 18-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 19-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 20-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 21-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 22-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 23-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 24-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 25-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 28-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 29-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 30-Apr-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 2-May-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 3-May-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 4-May-15
Orissa Banki Vegetables Other FAQ 2200 2500 2400 5-May-15
Orissa Banki Vegetables Other FAQ 2200 2500 2400 6-May-15
Orissa Banki Vegetables Other FAQ 2200 2500 2400 7-May-15
Orissa Banki Vegetables Other FAQ 2200 2500 2400 8-May-15
Orissa Banki Vegetables Other FAQ 2200 2600 2400 9-May-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 11-May-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 12-May-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 13-May-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 14-May-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 15-May-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 16-May-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 17-May-15
Orissa Banki Vegetables Other FAQ 2500 3000 2800 18-May-15
Orissa Banki Vegetables Other FAQ 2500 3000 2800 19-May-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 22-May-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 23-May-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 25-May-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 27-May-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 28-May-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 30-May-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 31-May-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 1-Jun-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 2-Jun-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 6-Jun-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 8-Jun-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 9-Jun-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 11-Jun-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 15-Jun-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 16-Jun-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 17-Jun-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 20-Jun-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 22-Jun-15
Orissa Banki Vegetables Other FAQ 3000 3500 3200 23-Jun-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 25-Jun-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 26-Jun-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 28-Jun-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 30-Jun-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 7-Jul-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 11-Jul-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 18-Jul-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 21-Jul-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 28-Jul-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 1-Aug-15
Orissa Banki Vegetables Other FAQ 3500 4000 3800 4-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2500 2200 8-Aug-15
Orissa Banki Vegetables Other FAQ 2200 2500 2400 10-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 11-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 22-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 25-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 29-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 30-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 31-Aug-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 1-Sep-15
Orissa Banki Vegetables Other FAQ 2000 2200 2100 4-Sep-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 5-Sep-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 10-Sep-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 11-Sep-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 12-Sep-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 15-Sep-15
Orissa Banki Vegetables Other FAQ 2000 2400 2200 19-Sep-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 22-Sep-15
Orissa Banki Vegetables Other FAQ 2200 2600 2400 26-Sep-15
Orissa Banki Vegetables Other FAQ 2200 2400 2300 29-Sep-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 1-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 4-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 6-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 7-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 12-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 13-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 14-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 16-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 17-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 19-Oct-15
Orissa Banki Vegetables Other FAQ 2400 2600 2500 20-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 22-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 24-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 27-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 31-Oct-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 1-Nov-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 3-Nov-15
Orissa Banki Vegetables Other FAQ 4200 4500 4400 10-Nov-15
Orissa Banki Vegetables Other FAQ 4800 5200 5000 21-Nov-15
Orissa Banki Vegetables Other FAQ 4500 4800 4600 24-Nov-15
Orissa Banki Vegetables Other FAQ 4500 5000 4800 28-Nov-15
Orissa Banki Vegetables Other FAQ 2200 2500 2400 1-Dec-15
Orissa Banki Vegetables Other FAQ 2400 2600 2500 4-Dec-15
Orissa Banki Vegetables Other FAQ 2200 2500 2400 7-Dec-15
Orissa Banki Vegetables Other FAQ 2800 3000 2900 12-Dec-15
Orissa Banki Vegetables Other FAQ 1400 1600 1500 27-Dec-15
Orissa Banki Vegetables Other FAQ 1000 1200 1100 29-Dec-15
Orissa Banki Vegetables Other FAQ 1800 2000 1900 30-Dec-15
Orissa Banki Vegetables Other FAQ 1200 1400 1300 2-Jan-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 5-Jan-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 7-Jan-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 8-Jan-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 9-Jan-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 11-Jan-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 12-Jan-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 14-Jan-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 15-Jan-16
Orissa Banki Vegetables Other FAQ 2000 2200 2100 16-Jan-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 18-Jan-16
Orissa Banki Vegetables Other FAQ 1800 2200 2000 22-Jan-16
Orissa Banki Vegetables Other FAQ 1600 1800 1700 23-Jan-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 28-Jan-16
Orissa Banki Vegetables Other FAQ 1000 1200 1100 30-Jan-16
Orissa Banki Vegetables Other FAQ 1400 1600 1500 4-Feb-16
Orissa Banki Vegetables Other FAQ 1400 1600 1500 5-Feb-16
Orissa Banki Vegetables Other FAQ 1500 1800 1600 6-Feb-16
Orissa Banki Vegetables Other FAQ 1300 1500 1400 11-Feb-16
Orissa Banki Vegetables Other FAQ 1000 1200 1100 14-Feb-16
Orissa Banki Vegetables Other FAQ 1000 1200 1100 19-Feb-16
Orissa Banki Vegetables Other FAQ 1300 1500 1400 21-Feb-16
Orissa Banki Vegetables Other FAQ 1200 1400 1300 22-Feb-16
Orissa Banki Vegetables Other FAQ 1300 1500 1400 23-Feb-16
Orissa Banki Vegetables Other FAQ 1300 1500 1400 24-Feb-16
Orissa Banki Vegetables Other FAQ 1400 1600 1500 25-Feb-16
Orissa Banki Vegetables Other FAQ 1400 1600 1500 26-Feb-16
Orissa Banki Vegetables Other FAQ 1400 1600 1500 27-Feb-16
Orissa Banki Vegetables Other FAQ 1400 1600 1500 28-Feb-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 1-Mar-16
Orissa Banki Vegetables Other FAQ 1600 1800 1700 4-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 5-Mar-16
Orissa Banki Vegetables Other FAQ 1500 1800 1600 7-Mar-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 8-Mar-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 10-Mar-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 11-Mar-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 12-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 13-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 14-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 15-Mar-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 16-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 17-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 21-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 22-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 25-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 27-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 30-Mar-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 2-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 4-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 5-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 6-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 9-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 11-Apr-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 13-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 17-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 19-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 21-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 23-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 25-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 26-Apr-16
Orissa Banki Vegetables Other FAQ 1800 2000 1900 27-Apr-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 28-Apr-16
Orissa Banki Vegetables Other FAQ 2000 2200 2100 29-Apr-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 1-May-16
Orissa Banki Vegetables Other FAQ 2400 2600 2500 4-May-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 6-May-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 7-May-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 8-May-16
Orissa Banki Vegetables Other FAQ 2000 2500 2200 9-May-16
Orissa Banki Vegetables Other FAQ 2500 2800 2600 10-May-16
Orissa Banki Vegetables Other FAQ 2500 2800 2600 11-May-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 12-May-16
Orissa Banki Vegetables Other FAQ 2500 2800 2600 15-May-16
Orissa Banki Vegetables Other FAQ 3000 3500 3200 16-May-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 18-May-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 20-May-16
Orissa Banki Vegetables Other FAQ 3000 3400 3200 21-May-16
Orissa Banki Vegetables Other FAQ 3000 3500 3200 22-May-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 23-May-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 24-May-16
Orissa Banki Vegetables Other FAQ 3400 3600 3500 28-May-16
Orissa Banki Vegetables Other FAQ 3200 3500 3400 30-May-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 31-May-16
Orissa Banki Vegetables Other FAQ 3000 3400 3200 1-Jun-16
Orissa Banki Vegetables Other FAQ 3400 3600 3500 2-Jun-16
Orissa Banki Vegetables Other FAQ 3000 3500 3200 4-Jun-16
Orissa Banki Vegetables Other FAQ 4000 4500 4200 10-Jun-16
Orissa Banki Vegetables Other FAQ 4000 4500 4200 13-Jun-16
Orissa Banki Vegetables Other FAQ 4000 4500 4200 27-Jun-16
Orissa Banki Vegetables Other FAQ 4000 4500 4200 29-Jun-16
Orissa Banki Vegetables Other FAQ 4000 4500 4200 2-Jul-16
Orissa Banki Vegetables Other FAQ 3000 3500 3200 20-Jul-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 5-Aug-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 16-Aug-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 22-Aug-16
Orissa Banki Vegetables Other FAQ 2800 3000 2900 26-Aug-16
Orissa Banki Vegetables Other FAQ 2400 2600 2500 9-Oct-16
Orissa Banki Vegetables Other FAQ 2000 2400 2200 12-Oct-16
Orissa Banki Vegetables Other FAQ 2000 2200 2100 14-Nov-16
Orissa Banki Vegetables Other FAQ 2000 2200 2100 18-Nov-16
Orissa Banki Vegetables Other FAQ 1000 1200 1100 19-Dec-16
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