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@theredpea
Last active October 29, 2019 01:07
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{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>hi</th>\n",
" <th>there</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hi there\n",
"0 1 2\n",
"3 4 5"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame({'hi':range(1,6),'there':range(2,7)}).iloc[[0,3]][['hi','there']]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"0 1 3\n",
"1 1 4"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame({'one':[1,1], 'two':[3,4]})"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1], dtype=int64)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame({'one':[1,1], 'two':[3,4]}).one.unique()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.5"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame({'one':[1,2], 'two':[3,4]}).one.mean()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"# Set the x-axis to a list of strings for each month.\n",
"x_axis = [\"Jan\", \"Feb\", \"Mar\", \"April\", \"May\", \"June\", \"July\", \"Aug\", \"Sept\", \"Oct\", \"Nov\", \"Dec\"]\n",
"\n",
"# Set the y-axis to a list of floats as the total fare in US dollars accumulated for each month.\n",
"y_axis = [10.02, 23.24, 39.20, 35.42, 32.34, 27.04, 43.82, 10.56, 11.85, 27.90, 20.71, 20.09]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x2725484be80>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"# Set the x-axis to a list of strings for each month.\n",
"x_axis = [\"Jan\", \"Feb\", \"Mar\", \"April\", \"May\", \"June\", \"July\", \"Aug\", \"Sept\", \"Oct\", \"Nov\", \"Dec\"]\n",
"\n",
"# Set the y-axis to a list of floats as the total fare in US dollars accumulated for each month.\n",
"y_axis = [10.02, 23.24, 39.20, 35.42, 32.34, 27.04, 43.82, 10.56, 11.85, 27.90, 20.71, 20.09]\n",
"# Create the plot\n",
"plt.style.use('ggplot')\n",
"plt.barh(list(reversed(x_axis)), list(reversed(y_axis)), color='magenta', label='Boston')\n",
"#plt.gca().invert_yaxis()\n",
"plt.xlabel('Fare($)')\n",
"plt.ylabel('Date')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"fig, ax = plt.subplots()\n",
"\n",
"# Using \"accumulation\" technique\n",
"# size = []; for y in y_axis: size.append(y * 10)\n",
"\n",
"# using list-comprehension technique:\n",
"# size = [y * 10 for y in y_axis]\n",
"\n",
"#Using the \"multiply Series / np.array by scalar\" technique\n",
"size = pd.Series(y_axis)*10\n",
"\n",
"ax.scatter(y_axis, x_axis, \n",
" s=size, \n",
" color='skyblue', \n",
" alpha=0.8, \n",
" marker='o',\n",
" edgecolors='k', \n",
" linewidths=3,\n",
" label='Boston')\n",
"ax.legend()\n",
"ax.invert_yaxis()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# condensed python \n",
"# explode_values = (0,0,0,0,0,0,0.2,0,0,0,0,0)\n",
"\n",
"# condensed python \n",
"# explode_values = [0]*6 + [0.2] + [0]*5\n",
"\n",
"# sensible, explain what you are doing\n",
"explode_values = [0.2 if x in ('July', 'Mar') else 0 for x in x_axis]\n",
"colors = [\"slateblue\", \"magenta\", \"lightblue\", \"green\", \"yellowgreen\", \"greenyellow\", \"yellow\", \"orange\", \"gold\", \"indianred\", \"tomato\", \"mistyrose\"]\n",
"\n",
"plt.subplots(figsize=(8,8))\n",
"plt.pie(y_axis, \n",
" labels=x_axis, \n",
" explode=explode_values, \n",
" autopct='%.1f%%', \n",
" colors=colors, \n",
" shadow=True,\n",
" counterclock=False,\n",
" startangle=90)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"from matplotlib.ticker import MultipleLocator\n",
"\n",
"# plt.\n",
"plt.barh(x_axis, y_axis)\n",
"plt.gca().xaxis.set_major_locator(MultipleLocator(5))\n",
"plt.gca().xaxis.set_minor_locator(MultipleLocator(1))\n",
"# plt.xticks(np.arange(0,51, step=5.0))\n",
"# Create minor ticks at an increment of 1.\n",
"plt.gca().invert_yaxis()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.7"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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