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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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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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\nxZIlS1AqldTU1LB//3727t1Ld3c327Ztw9DQUM76VCgUzJ07l6ioKIaHh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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 | |
} |
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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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