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ischurov/cpk-python-2018-lecture7.ipynb

Created October 19, 2018 15:50
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cpk-python-2018-lecture7.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"students = pd.DataFrame([['Alice', 973243],\n",
" ['Bob', 123212],\n",
" ['Claudia', 7651231]], columns=['student', 'id'])\n",
"\n",
"gradebook = pd.DataFrame([[123212, 5, 4],\n",
" [7651231, 4, 5],\n",
" [7654599, 3, 3]], columns=['id_number', \n",
" 'Algebra', 'Calculus'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>student</th>\n",
" <th>id</th>\n",
" <th>id_number</th>\n",
" <th>Algebra</th>\n",
" <th>Calculus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bob</td>\n",
" <td>123212</td>\n",
" <td>123212</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Claudia</td>\n",
" <td>7651231</td>\n",
" <td>7651231</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" student id id_number Algebra Calculus\n",
"0 Bob 123212 123212 5 4\n",
"1 Claudia 7651231 7651231 4 5"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"students.merge(gradebook, left_on='id', right_on='id_number')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>student</th>\n",
" <th>id</th>\n",
" <th>id_number</th>\n",
" <th>Algebra</th>\n",
" <th>Calculus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>973243</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bob</td>\n",
" <td>123212</td>\n",
" <td>123212.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Claudia</td>\n",
" <td>7651231</td>\n",
" <td>7651231.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" student id id_number Algebra Calculus\n",
"0 Alice 973243 NaN NaN NaN\n",
"1 Bob 123212 123212.0 5.0 4.0\n",
"2 Claudia 7651231 7651231.0 4.0 5.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"students.merge(gradebook, how='left', left_on='id', right_on='id_number')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"table = students.merge(gradebook, how='outer', left_on='id',\n",
" right_on='id_number')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = float(\"NaN\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if x == float(\"NaN\"):\n",
" print(\"x is NaN!\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(\"NaN\") == float(\"NaN\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(\"inf\") == float(\"inf\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.isnan(x)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 True\n",
"1 False\n",
"2 False\n",
"3 True\n",
"dtype: bool"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.isnull().any(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>student</th>\n",
" <th>id</th>\n",
" <th>id_number</th>\n",
" <th>Algebra</th>\n",
" <th>Calculus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bob</td>\n",
" <td>123212.0</td>\n",
" <td>123212.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Claudia</td>\n",
" <td>7651231.0</td>\n",
" <td>7651231.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" student id id_number Algebra Calculus\n",
"1 Bob 123212.0 123212.0 5.0 4.0\n",
"2 Claudia 7651231.0 7651231.0 4.0 5.0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>student</th>\n",
" <th>id</th>\n",
" <th>id_number</th>\n",
" <th>Algebra</th>\n",
" <th>Calculus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>973243.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bob</td>\n",
" <td>123212.0</td>\n",
" <td>123212.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Claudia</td>\n",
" <td>7651231.0</td>\n",
" <td>7651231.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" student id id_number Algebra Calculus\n",
"0 Alice 973243.0 0.0 0.0 0.0\n",
"1 Bob 123212.0 123212.0 5.0 4.0\n",
"2 Claudia 7651231.0 7651231.0 4.0 5.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.dropna(subset=['student']).fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"journal = pd.DataFrame([['Alice', 'Algebra', 5],\n",
" ['Bob', 'Calculus', 3],\n",
" ['Alice', 'Calculus', 4]],\n",
" columns=['student', 'course', 'grade']\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>student</th>\n",
" <th>course</th>\n",
" <th>grade</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>Algebra</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bob</td>\n",
" <td>Calculus</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alice</td>\n",
" <td>Calculus</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" student course grade\n",
"0 Alice Algebra 5\n",
"1 Bob Calculus 3\n",
"2 Alice Calculus 4"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"journal"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>course</th>\n",
" <th>Algebra</th>\n",
" <th>Calculus</th>\n",
" </tr>\n",
" <tr>\n",
" <th>student</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alice</th>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bob</th>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"course Algebra Calculus\n",
"student \n",
"Alice 5.0 4.0\n",
"Bob NaN 3.0"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"journal.pivot(index='student', \n",
" columns='course', \n",
" values='grade')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"new_table = table.dropna().drop(['id', 'id_number'], \n",
" axis='columns')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"new_table.iloc[:, 1:] = new_table.iloc[:, 1:].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>student</th>\n",
" <th>Algebra</th>\n",
" <th>Calculus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bob</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Claudia</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" student Algebra Calculus\n",
"1 Bob 5 4\n",
"2 Claudia 4 5"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_table"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>student</th>\n",
" <th>course</th>\n",
" <th>grade</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bob</td>\n",
" <td>Algebra</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Claudia</td>\n",
" <td>Algebra</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Bob</td>\n",
" <td>Calculus</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Claudia</td>\n",
" <td>Calculus</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" student course grade\n",
"0 Bob Algebra 5\n",
"1 Claudia Algebra 4\n",
"2 Bob Calculus 4\n",
"3 Claudia Calculus 5"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_table.melt(id_vars='student', value_vars=['Algebra',\n",
" 'Calculus'],\n",
" var_name='course', value_name='grade')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"journal = pd.DataFrame([['Alice', 'Algebra', 5],\n",
" ['Bob', 'Calculus', 3],\n",
" ['Alice', 'Calculus', 4],\n",
" ['Alice', 'Calculus', 5]\n",
" ],\n",
" columns=['student', 'course', 'grade']\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>course</th>\n",
" <th>Algebra</th>\n",
" <th>Calculus</th>\n",
" </tr>\n",
" <tr>\n",
" <th>student</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alice</th>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bob</th>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"course Algebra Calculus\n",
"student \n",
"Alice 5.0 5.0\n",
"Bob NaN 3.0"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"journal.pivot_table(index='student', \n",
" columns='course', \n",
" values='grade', aggfunc='last')"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url = \"https://bit.ly/2FfPmiX\""
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"tables = pd.read_html(url, header=0)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(tables)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(tables)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = tables[0]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"STATION_ID int64\n",
"STATION_NM object\n",
"DATE_OBS object\n",
"TMPMAX float64\n",
"Q int64\n",
"TMPMIN float64\n",
"Q.1 int64\n",
"TMPMN float64\n",
"Q.2 int64\n",
"PRECIP float64\n",
"Q.3 int64\n",
"D int64\n",
"dtype: object"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STATION_ID</th>\n",
" <th>STATION_NM</th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>Q</th>\n",
" <th>TMPMIN</th>\n",
" <th>Q.1</th>\n",
" <th>TMPMN</th>\n",
" <th>Q.2</th>\n",
" <th>PRECIP</th>\n",
" <th>Q.3</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-01</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-02</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-03</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-04</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-05</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATION_ID STATION_NM DATE_OBS TMPMAX Q TMPMIN Q.1 TMPMN Q.2 \\\n",
"0 27612 МОСКВА ВДНХ 1948-01-01 NaN 9 NaN 9 NaN 9 \n",
"1 27612 МОСКВА ВДНХ 1948-01-02 NaN 9 NaN 9 NaN 9 \n",
"2 27612 МОСКВА ВДНХ 1948-01-03 NaN 9 NaN 9 NaN 9 \n",
"3 27612 МОСКВА ВДНХ 1948-01-04 NaN 9 NaN 9 NaN 9 \n",
"4 27612 МОСКВА ВДНХ 1948-01-05 NaN 9 NaN 9 NaN 9 \n",
"\n",
" PRECIP Q.3 D \n",
"0 NaN 9 9 \n",
"1 NaN 9 9 \n",
"2 NaN 9 9 \n",
"3 NaN 9 9 \n",
"4 NaN 9 9 "
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"df_prep = df.dropna().drop(['STATION_ID', 'STATION_NM', 'Q',\n",
" 'Q.1', 'Q.2', 'Q.3', 'D'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1acdb2b0>"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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V8MdUiHGvbQtz5tQqtveIcg47Y+uZWT6TTf2byMt50nlx3K9O/xV/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guwngoZHJSGWVutUcs0brx67dgER0YrvHGiTCZbZgBbRA9/dsIv+Hii267X/1r4YY82avh5JXyR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\nwhcRWSaU8EVElgklfBGRZUIJX0Rkmfh/FP8LpdXJq5YAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1aa8a8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_prep[:365].plot(y=['TMPMAX', 'TMPMIN', 'TMPMN'])"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c1cd358>"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YGKZFICCJIxaMcWTXkWye3OxGjw7ZzEeE/8xwiYuOX+RadhvhEH67zg1FM6jpJokIPJl9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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1c1b2a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(df_prep[:3650].drop('DATE_OBS', axis=1)\n",
" .rolling(window=30).mean().plot())"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"df_prep.index = pd.to_datetime(df_prep['DATE_OBS'])"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c7a6908>"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YPaUnn8cb0rLOU6gl4IuieFoQBCd12KqOPL5W/6zrmZmZ9OrVi19//ZUPP/zw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\nuj9B+8j27D65myHNhrjkbsFl5j2ufeOEREOch2LaGgyGKk+5Rvgi8g8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XYDAYKocqNy3T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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1c763e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(df_prep[:365].drop('DATE_OBS', axis=1)\n",
" .rolling(window=30).mean().plot())"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DATE_OBS\n",
"1948-12-11 -4.5\n",
"1948-12-12 -8.7\n",
"1948-12-13 -12.3\n",
"1948-12-14 -13.2\n",
"1948-12-15 -9.2\n",
"1948-12-16 -6.7\n",
"1948-12-17 -14.4\n",
"1948-12-18 -17.5\n",
"1948-12-19 -17.3\n",
"1948-12-20 -10.9\n",
"1948-12-21 -7.8\n",
"1948-12-22 -7.2\n",
"1948-12-23 -9.6\n",
"1948-12-24 -9.7\n",
"1948-12-25 -12.5\n",
"1948-12-26 -13.2\n",
"1948-12-27 -6.3\n",
"1948-12-28 -3.5\n",
"1948-12-29 -4.9\n",
"1948-12-30 -6.5\n",
"1948-12-31 -5.3\n",
"1949-01-01 -6.7\n",
"1949-01-02 -6.7\n",
"1949-01-03 -2.1\n",
"1949-01-04 0.9\n",
"1949-01-05 -0.9\n",
"1949-01-06 -3.2\n",
"1949-01-07 -5.1\n",
"1949-01-08 -3.0\n",
"1949-01-09 -1.6\n",
" ... \n",
"2006-10-02 7.2\n",
"2006-10-03 10.9\n",
"2006-10-04 10.3\n",
"2006-10-05 11.6\n",
"2006-10-06 9.0\n",
"2006-10-07 7.9\n",
"2006-10-08 7.1\n",
"2006-10-09 9.9\n",
"2006-10-10 8.5\n",
"2006-10-11 6.0\n",
"2006-10-12 0.4\n",
"2006-10-13 5.1\n",
"2006-10-14 4.7\n",
"2006-10-15 -1.4\n",
"2006-10-16 -1.7\n",
"2006-10-17 -4.1\n",
"2006-10-18 -1.0\n",
"2006-10-19 3.8\n",
"2006-10-20 2.3\n",
"2006-10-21 0.5\n",
"2006-10-22 3.9\n",
"2006-10-23 9.1\n",
"2006-10-24 9.9\n",
"2006-10-25 11.4\n",
"2006-10-26 5.8\n",
"2006-10-27 2.7\n",
"2006-10-28 4.1\n",
"2006-10-29 1.4\n",
"2006-10-30 -1.9\n",
"2006-10-31 -7.7\n",
"Name: TMPMIN, Length: 21142, dtype: float64"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_prep.loc[:, \"TMPMIN\"]"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DATE_OBS 1949-01-01\n",
"TMPMAX -2.1\n",
"TMPMIN -6.7\n",
"TMPMN -4.2\n",
"PRECIP 0\n",
"Name: 1949-01-01 00:00:00, dtype: object"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_prep.loc[\"1949-01-01\"]"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE_OBS</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1949-01-01</th>\n",
" <td>1949-01-01</td>\n",
" <td>-2.1</td>\n",
" <td>-6.7</td>\n",
" <td>-4.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP\n",
"DATE_OBS \n",
"1949-01-01 1949-01-01 -2.1 -6.7 -4.2 0.0"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_prep[\"1949-01-01\":\"1949-01-01\"]"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d064550>"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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osHWtkD3aJ2SuyxXSRZcQPVQyUhLcsd7DTXFlLSKQFcBTR+k4qOauxCTFlQ4G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\nCndFUZQY5P8DZu4Im//CyVkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1ce4ef60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(df_prep.drop('DATE_OBS', axis=1)\n",
" .rolling(window=365*10).mean().plot(y='TMPMN'))"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948,\n",
" ...\n",
" 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006],\n",
" dtype='int64', name='DATE_OBS', length=21142)"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_prep.index.year"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_prep['year'] = df_prep.index.year\n",
"df_prep['month'] = df_prep.index.month\n",
"df_prep['day'] = df_prep.index.day"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [],
"source": [
"corr = df_prep.pivot_table(index='year', columns='month', \n",
" values='TMPMN').dropna().corr()"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x2d59dc50>"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8NbxBYDAinszW11Il4ZnZgcVN2ioi4lVJr0h6f0Q8D5xN6XkZMzuAudNifF8A7sh6aLcD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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2d6629e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.imshow(corr, cmap='seismic', vmin=-1, vmax=1)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"html = \"\"\"\n",
"<h1>Hello</h1>\n",
"<p>This is a paragraph with <a href=\"http://hse.ru\">link</a>.</p>\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<h1>Hello</h1>\n",
"<p>This is a paragraph with <a href=\"http://hse.ru\">link</a>.</p>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(html)"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [],
"source": [
"soup = BeautifulSoup(html, 'lxml')"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<html><body><h1>Hello</h1>\n",
"<p>This is a paragraph with <a href=\"http://hse.ru\">link</a>.</p>\n",
"</body></html>"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soup"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<p>This is a paragraph with <a href=\"http://hse.ru\">link</a>.</p>"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soup.find(\"p\")"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<p>This is a paragraph with <a href=\"http://hse.ru\">link</a>.</p>]"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soup.find_all(\"p\")"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<a href=\"http://hse.ru\">link</a>"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soup.find(\"p\").find(\"a\")"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'http://hse.ru'"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soup.find(\"a\")['href']"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r = requests.get(\"https://www.hse.ru/\")"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Response [200]>"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'<!DOCTYPE html>\\n<html lang=\"ru\">\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n<head>\\n\\t<title>Национальный исследовательский университет Высшая школа экономики</title>\\n\\t<meta charset=\"utf-8\" />\\n\\t<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\\n\\t<meta http-equiv=\"X-UA-Compatible\" content=\"IE=Edge\" />\\n\\t<meta name=\"ya'"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.text[:300]"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [],
"source": [
"soup = BeautifulSoup(r.text, 'lxml')"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ВШЭ выросла на 50 позиций в рейтинге ТНЕ по социальными наукам и сохранила позиции по бизнесу и экономике\n",
"ВШЭ заняла 37 место в рейтинге QS BRICS\n"
]
}
],
"source": [
"for h3 in soup.find_all(\"h3\", class_='post__title'):\n",
" if 'ВШЭ' in h3.text:\n",
" print(h3.text.strip())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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