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notebooks/pandas.ipynb
Klesh Wong d4ae1f226d [misc] initial commit
2020-08-12 17:42:23 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.1.0'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"pd.__version__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>Name</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>22</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>35</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>58</td>\n",
" <td>female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Age Sex\n",
"0 Braund, Mr. Owen Harris 22 male\n",
"1 Allen, Mr. William Henry 35 male\n",
"2 Bonnell, Miss. Elizabeth 58 female"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# A DataFrame is a 2d data structure that can store data of different types\n",
"# in columns. It is similar to a spreadsheet, a SQL table or data.frame in R\n",
"df = pd.DataFrame({\n",
" \"Name\": [\"Braund, Mr. Owen Harris\",\n",
" \"Allen, Mr. William Henry\",\n",
" \"Bonnell, Miss. Elizabeth\"],\n",
" \"Age\": [22, 35, 58],\n",
" \"Sex\": [\"male\", \"male\", \"female\"],\n",
"})\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"ages from DataFrame\n",
"0 22\n",
"1 35\n",
"2 58\n",
"Name: Age, dtype: int64\n",
"\n",
"ages from scratch\n",
"0 22\n",
"1 35\n",
"2 58\n",
"Name: Age, dtype: int64\n"
]
}
],
"source": [
"# Each column in a DataFrame is a Series\n",
"ages = df[\"Age\"]\n",
"print('\\nages from DataFrame')\n",
"print(ages)\n",
"\n",
"# You can create a Series from scratch as well:\n",
"ages = pd.Series([22, 35, 58], name=\"Age\")\n",
"print('\\nages from scratch')\n",
"print(ages)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"58"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Series operations\n",
"ages.max()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>38.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>18.230012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>22.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>28.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>35.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>46.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>58.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age\n",
"count 3.000000\n",
"mean 38.333333\n",
"std 18.230012\n",
"min 22.000000\n",
"25% 28.500000\n",
"50% 35.000000\n",
"75% 46.500000\n",
"max 58.000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# DataFame statistic: textual columns will be omitted\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Working with csv\n",
"# https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv\n",
"titanic = pd.read_csv(\"data/titanic.csv\")\n",
"titanic.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PassengerId 891 non-null int64 \n",
" 1 Survived 891 non-null int64 \n",
" 2 Pclass 891 non-null int64 \n",
" 3 Name 891 non-null object \n",
" 4 Sex 891 non-null object \n",
" 5 Age 714 non-null float64\n",
" 6 SibSp 891 non-null int64 \n",
" 7 Parch 891 non-null int64 \n",
" 8 Ticket 891 non-null object \n",
" 9 Fare 891 non-null float64\n",
" 10 Cabin 204 non-null object \n",
" 11 Embarked 889 non-null object \n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.7+ KB\n"
]
}
],
"source": [
"# Get technical summary of DataFrame\n",
"titanic.info()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Multiple-Columns subset, result is a new DataFrame\n",
"age_sex = titanic[['Age', 'Sex']]\n",
"type(age_sex)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(891, 2)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_sex.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>female</td>\n",
" <td>58.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113783</td>\n",
" <td>26.5500</td>\n",
" <td>C103</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Andersson, Mr. Anders Johan</td>\n",
" <td>male</td>\n",
" <td>39.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>347082</td>\n",
" <td>31.2750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
" <td>female</td>\n",
" <td>55.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>248706</td>\n",
" <td>16.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"1 2 1 1 \n",
"6 7 0 1 \n",
"11 12 1 1 \n",
"13 14 0 3 \n",
"15 16 1 2 \n",
"\n",
" Name Sex Age SibSp \\\n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"6 McCarthy, Mr. Timothy J male 54.0 0 \n",
"11 Bonnell, Miss. Elizabeth female 58.0 0 \n",
"13 Andersson, Mr. Anders Johan male 39.0 1 \n",
"15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"1 0 PC 17599 71.2833 C85 C \n",
"6 0 17463 51.8625 E46 S \n",
"11 0 113783 26.5500 C103 S \n",
"13 5 347082 31.2750 NaN S \n",
"15 0 248706 16.0000 NaN S "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filtering rows\n",
"\n",
"age_above_35 = titanic[titanic['Age'] > 35]\n",
"age_above_35.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.series.Series'>\n"
]
},
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 False\n",
"3 False\n",
"4 False\n",
" ... \n",
"886 False\n",
"887 False\n",
"888 False\n",
"889 False\n",
"890 False\n",
"Name: Age, Length: 891, dtype: bool"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How filtering expression works\n",
"exp = titanic['Age'] > 35 # output a mask Series\n",
"print(type(exp))\n",
"exp"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3</td>\n",
" <td>Sandstrom, Miss. Marguerite Rut</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>3</td>\n",
" <td>Saundercock, Mr. William Henry</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>3</td>\n",
" <td>Andersson, Mr. Anders Johan</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>3</td>\n",
" <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2</td>\n",
" <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>3</td>\n",
" <td>Rice, Master. Eugene</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2</td>\n",
" <td>Williams, Mr. Charles Eugene</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>3</td>\n",
" <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3</td>\n",
" <td>Masselmani, Mrs. Fatima</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2</td>\n",
" <td>Fynney, Mr. Joseph J</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2</td>\n",
" <td>Beesley, Mr. Lawrence</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3</td>\n",
" <td>McGowan, Miss. Anna \"Annie\"</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1</td>\n",
" <td>Sloper, Mr. William Thompson</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>3</td>\n",
" <td>Palsson, Miss. Torborg Danira</td>\n",
" <td>female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass Name Sex\n",
"9 2 Nasser, Mrs. Nicholas (Adele Achem) female\n",
"10 3 Sandstrom, Miss. Marguerite Rut female\n",
"11 1 Bonnell, Miss. Elizabeth female\n",
"12 3 Saundercock, Mr. William Henry male\n",
"13 3 Andersson, Mr. Anders Johan male\n",
"14 3 Vestrom, Miss. Hulda Amanda Adolfina female\n",
"15 2 Hewlett, Mrs. (Mary D Kingcome) female\n",
"16 3 Rice, Master. Eugene male\n",
"17 2 Williams, Mr. Charles Eugene male\n",
"18 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female\n",
"19 3 Masselmani, Mrs. Fatima female\n",
"20 2 Fynney, Mr. Joseph J male\n",
"21 2 Beesley, Mr. Lawrence male\n",
"22 3 McGowan, Miss. Anna \"Annie\" female\n",
"23 1 Sloper, Mr. William Thompson male\n",
"24 3 Palsson, Miss. Torborg Danira female"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filtering by isin, can not use or/and operator in this case\n",
"class_23 = titanic[(titanic[\"Pclass\"] == 2) | (titanic[\"Pclass\"] == 3)]\n",
"# Filtering by notna\n",
"age_no_na = titanic[titanic['Age'].notna()]\n",
"# Filtering and Subset: df.loc[rows_filtering, cols_filtering]\n",
"adult_names = titanic.loc[titanic[\"Age\"] > 35, \"Name\"]\n",
"# Select by region: df.iloc[row_start:row_end, col_start:col_end]\n",
"titanic.iloc[9:25, 2:5]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>anonymous</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>anonymous</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>anonymous</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp Parch \\\n",
"0 anonymous male 22.0 1 0 \n",
"1 anonymous female 38.0 1 0 \n",
"2 anonymous female 26.0 0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 \n",
"4 Allen, Mr. William Henry male 35.0 0 0 \n",
"\n",
" Ticket Fare Cabin Embarked \n",
"0 A/5 21171 7.2500 NaN S \n",
"1 PC 17599 71.2833 C85 C \n",
"2 STON/O2. 3101282 7.9250 NaN S \n",
"3 113803 53.1000 C123 S \n",
"4 373450 8.0500 NaN S "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Assign new values to selection base on loc/iloc\n",
"titanic.iloc[0:3, 3] = \"anonymous\"\n",
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1035 entries, 0 to 1034\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 datetime 1035 non-null object \n",
" 1 station_antwerp 95 non-null float64\n",
" 2 station_paris 1004 non-null float64\n",
" 3 station_london 969 non-null float64\n",
"dtypes: float64(3), object(1)\n",
"memory usage: 32.5+ KB\n",
"None\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 1035 entries, 2019-05-07 02:00:00 to 2019-06-21 02:00:00\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 station_antwerp 95 non-null float64\n",
" 1 station_paris 1004 non-null float64\n",
" 2 station_london 969 non-null float64\n",
"dtypes: float64(3)\n",
"memory usage: 32.3 KB\n",
"None\n"
]
},
{
"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>station_antwerp</th>\n",
" <th>station_paris</th>\n",
" <th>station_london</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-05-07 02:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 03:00:00</th>\n",
" <td>50.5</td>\n",
" <td>25.0</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 04:00:00</th>\n",
" <td>45.0</td>\n",
" <td>27.7</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 05:00:00</th>\n",
" <td>NaN</td>\n",
" <td>50.4</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 06:00:00</th>\n",
" <td>NaN</td>\n",
" <td>61.9</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" station_antwerp station_paris station_london\n",
"datetime \n",
"2019-05-07 02:00:00 NaN NaN 23.0\n",
"2019-05-07 03:00:00 50.5 25.0 19.0\n",
"2019-05-07 04:00:00 45.0 27.7 19.0\n",
"2019-05-07 05:00:00 NaN 50.4 16.0\n",
"2019-05-07 06:00:00 NaN 61.9 NaN"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plotting\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/air_quality_no2.csv\n",
"air_quality = pd.read_csv(\"data/air_quality_no2.csv\")\n",
"print(air_quality.info())\n",
"air_quality = pd.read_csv(\"data/air_quality_no2.csv\", index_col=0, parse_dates=[\"datetime\"])\n",
"print(air_quality.info())\n",
"air_quality.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 921.6x691.2 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 921.6x691.2 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 921.6x691.2 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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fn8OHD2f//v3Zs2dPpqamuo4GwDIUeQAyPz+fAwcOZHR0NENDQxkdHc2BAwecRx5gDbNGHgDnkQdokBl5AJI4jzxAaxR5AJxHHqBBijwA6fV6+fKXv3zc2Je//GXnkQdYwxR5ADI+Pp4dO3bk6quvzsMPP5yrr746O3bsyPj4eNfRAFiGIg9AZmdns3379uzevTvnnntudu/ene3bt2d2drbraAAsQ5EHIPPz87n88suPG7v88sudfhJgDXP6SQBy0UUX5YYbbsiHPvSho6effN3rXuf0kwBrmBl5AJIkpZSTPgZgbVHkAci9996bl770pbniiityzjnn5IorrshLX/pSp58EWMMUeQBy3nnn5c4778yGDUu/FjZs2JA777wz5513XrfBAFiWIg9ADh48mFprrr322jz44IO59tprU2vNwYMHu44GwDIUeQBSa83LX/7y7N69O+edd152796dl7/85am1dh0NgGUo8gAkSfbt25fnP//52bBhQ57//Odn3759XUcC4CQUeQCSJAsLC3nooYdy5MiRPPTQQ1lYWOg6EnAKMzMzGR0dzdDQUEZHRzMzM9N1JM4g55EH4KiHHnrouK/A2jUzM5N+v5/p6emj13+YmJhIkmzbtq3jdJwJZuQBSJJceOGFOXLkSJLkyJEjufDCCztOBJzM1NRUpqenMz4+nuHh4YyPj2d6ejpTU1NdR+MMUeQBSJLcf//9uf766/Pggw/m+uuvz/333991JOAk5ufnMzY2dtzY2NhY5ufnO0rEmabIA5AkGR4ezvvf//6cd955ef/735/h4eGuIwEn0ev1Mjc3d9zY3Nxcer1eR4k40xR5AJIkhw8fzvnnn59SSs4///wcPny460jASfT7/UxMTGR2djaLi4uZnZ3NxMRE+v1+19E4QxzsCkBKKRkfH893vvOdPPDAA3ne856X0dHRfPrTn+46GrCMJw5onZyczPz8fHq9Xqamphzouo6UtXCxj61bt9a9e/d2HQNg3dqwYUM2bNiQW265Jdddd1127dqVG264IUeOHDl6ACwAZ14p5a5a69YTPWdGHoC85CUvyYtf/OK84x3vyFvf+tZs2rQpv/zLv5yvfvWrXUcDYBnWyAOQfr+fL3zhC/nEJz6Rxx57LJ/4xCfyhS98wVpbgDVMkQcg27Zty9TUVCYnJzMyMpLJyUlrbaEBruy6vllaA0CSpTKvuEM7XNkVB7sCADRodHQ0O3fuzPj4+NGx2dnZTE5OZt++fR0mYzWd7GBXRR4AoEFDQ0M5dOjQcRdvW1xczMjIiOtAnEVOVuStkQcAaJAru6LIAwA0yJVdcbArAECDXNkVa+QBzhKllK4jPMVa+B0D0DJr5AHWgVrrqtwu3f6xVdsWAKePIg8AAA1S5AEAoEGKPAAANEiRBwCABinyAADQIEUeAAAapMgDADRqZmYmo6OjGRoayujoaGZmZrqOxBnkyq4AAA2amZlJv9/P9PR0xsbGMjc3l4mJiSRxddd1wow8AECDpqamMj09nfHx8QwPD2d8fDzT09OZmprqOhpniCIPANCg+fn5jI2NHTc2NjaW+fn5jhJxpinyAAAN6vV6mZubO25sbm4uvV6vo0ScaYo8AECD+v1+JiYmMjs7m8XFxczOzmZiYiL9fr/raJwhDnYFAGjQEwe0Tk5OZn5+Pr1eL1NTUw50XUcUeQCARm3btk1xX8csrQEAgAatqMiXUs4rpfxhKeU/lFLmSyl/r5RyfinlU6WUrw6+Pne1wgIAAEtWOiN/a5J/W2v94SQ/mmQ+yY1J7qy1vjjJnYPHNKaUsuZuAMDxJicnMzIyklJKRkZGMjk52XUkzqBnXORLKc9J8jNJppOk1vpYrfXBJL+S5PbBy25P8tqVRaQLtdZVuV26/WOrti0A4P83OTmZXbt25aabbsrCwkJuuumm7Nq1S5lfR1YyI/+iJA8k+VellM+XUt5fStmc5MJa632D13wnyYUrDQkAwPFuu+227NixI295y1vy7Gc/O295y1uyY8eO3HbbbV1H4wxZSZHfmOTHk7y31vpjSRbypGU0dWka9YRTqaWUa0spe0spex944IEVxAAAWH8effTRXHfddceNXXfddXn00Uc7SsSZtpIifyDJgVrrZweP/zBLxf7+Usrzk2Tw9bsnenOt9X211q211q0XXHDBCmIAAKw/mzZtyq5du44b27VrVzZt2tRRIs60Z1zka63fSfKtUsrlg6ErknwpyUeTXDUYuyrJR1aUEACAp7jmmmvytre97bgTQ7ztbW/LNddc03U0zpCVXhBqMskHSynnJPl6kjdm6Y+DPyilTCS5J8mvrfAzAAB4kq985SuptWbDhg05cuTI0a9f+cpXuo7GGbKi00/WWv96sDzmR2qtr621Hqy1/m2t9Ypa64trrT9Xa/271QoLAMCST33qU7n++utz+PDh1Fpz+PDhXH/99fnUpz7VdTTOEFd2BQBoUK01N99883FjN998s1M2ryOKPABAg0opefvb337c2Nvf/nYXUVxHVrpGHgCADvz8z/983vve9+bDH/5wDh48mOc+97k5ePBgXvnKV3YdjTPEjDwAQIPe8IY3ZGRkJAcPHkySHDx4MCMjI3nDG97QbTDOGEUeAKBBU1NT+fjHP55a69Hbxz/+8UxNTXUdjTNEkQcAaND8/HzGxsaOGxsbG8v8/HxHiTjTFHkAgAb1er3Mzc0dNzY3N5der9dRIs40RR4AoEH9fj8TExOZnZ3N4uJiZmdnMzExkX6/33U0zhBnrQEAaNC2bduSJJOTk5mfn0+v18vU1NTRcc5+ijwAQKO2bdumuK9jltYAAECDFHkAAGiQIg8AAA1S5AEAoEGKPAAANEiRBwCABinyAADQIEUeAAAapMgDAECDFHkAAGiQIg8AAA1S5AEAGjUzM5PR0dEMDQ1ldHQ0MzMzXUfiDNrYdQAAAJ6+mZmZ9Pv9TE9PZ2xsLHNzc5mYmEiSbNu2reN0nAlm5AEAGjQ1NZXp6emMj49neHg44+PjmZ6eztTUVNfROEMUeQCABs3Pz2dsbOy4sbGxsczPz3eUiDNNkQcAaFCv18vc3NxxY3Nzc+n1eh0l4kyzRh4AoEH9fj9XXnllNm/enHvuuSeXXnppFhYWcuutt3YdjTPEjDwAQONKKV1HoAOKPABAg6amprJnz57s378/hw8fzv79+7Nnzx4Hu64jijwAQIMc7IoiDwDQIAe74mBXAIAG9fv9vOpVr8ri4uLRseHh4dx+++0dpuJMMiMPANCgm2++OYuLi9myZUuSZMuWLVlcXMzNN9/ccTLOFEUeAKBBd999d17zmtfk4YcfTq01Dz/8cF7zmtfk7rvv7joaZ4giDwDQqOnp6ZM+5uymyAMANGpiYuKkjzm7OdgVAKBBL3vZy/LRj340Q0NDOXLkSDZs2JAjR47kZS97WdfROEPMyAMANOgVr3hFkuTIkSPHfX1inLOfIg8A0KDbbrst7373u1NrPXp797vfndtuu63raJwhijwAQIMeffTRXHfddceNXXfddXn00Uc7SsSZpsgDADRo06ZN2bVr13Fju3btyqZNmzpKxJnmYFcAgAZdc8012b59e5Klmfhdu3Zl+/btT5ml5+ylyAMANGjnzp1Jkne84x1561vfmk2bNuW66647Os7ZT5EHAGjUzp07Ffd1zBp5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAY5aw0AQEdKKV1HeIpaa9cR+D6ZkQcA6EitdVVul27/2Kpti3Yo8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBKy7ypZShUsrnSykfGzx+USnls6WUr5VS9pRSzll5TAAA4FirMSP/piTzxzzekeR3aq0/lORgkolV+AwAAOAYKyrypZRLkrw6yfsHj0uSv5/kDwcvuT3Ja1fyGQAAwFOtdEb+nye5IcmRweMfTPJgrfXxweMDSS5e4WcAAABP8oyLfCnlv0ny3VrrXc/w/deWUvaWUvY+8MADzzQGAACsSyuZkf+pJK8ppXwjyYeztKTm1iTnlVI2Dl5zSZJvn+jNtdb31Vq31lq3XnDBBSuIAQAA688zLvK11rfXWi+ptV6W5NeTfLrW+voks0l+dfCyq5J8ZMUpAQCA45yO88hvT/KWUsrXsrRmfvo0fAYAAKxrG0/9klOrtX4myWcG97+e5CdWY7sAAMCJubIrAAA0SJEHAIAGKfIAANAgRR4AABqkyAMAQIMUeQAAaJAiDwAADVLkAQCgQYo8AAA0SJEHAIAGKfIAANAgRR4AABqkyAMAQIMUeQAAaNDGrgMArHc/+s4/yUPfW+w6xnEuu/GOriMc9ZxnDecLv/3KrmMArDmKPEDHHvreYr7xrld3HWPNWkt/VACsJZbWAABAgxR5AABokCIPAAANUuQBAKBBijwAADTIWWvOMk5jd3JOYwcAnC0U+bOM09id3Fr6owIAYCUsrQEAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRoY9cBAABa9KPv/JM89L3FrmMcddmNd3Qd4ajnPGs4X/jtV3Yd46ynyAMAPAMPfW8x33jXq7uOsSatpT8qzmaW1gAAQIMUeQAAaJAiDwAADVLkAQCgQYo8AAA0SJEHAIAGKfIAANAgRR4AABqkyAMAQIMUeQAAaJAiDwAADVLkAQCgQYo8AAA0SJEHAIAGKfIAANAgRR4AABqkyAMAQIMUeQAAaJAiDwAADVLkAQCgQYo8AAA0SJEHAIAGKfIAANAgRR4AABqkyAMAQIMUeQAAaJAiDwAADdrYdQAAgBad27sxL7v9xq5jrEnn9pLk1V3HOOsp8gAAz8DD8+/KN96lrJ7IZTfe0XWEdcHSGgAAaJAiDwAADbK0BqBj1tmenLW2ACemyAN0zDrbk7PWFuDELK0BAIAGKfIAANAgRR4AABqkyAMAQIMc7HqWcfaLk3P2CwDgbKHIn2Wc/eLknP0CADhbWFoDAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANesZFvpTyglLKbCnlS6WUL5ZS3jQYP7+U8qlSylcHX5+7enEBAIBkZTPyjyd5a631JUl+MslvllJekuTGJHfWWl+c5M7BYwAAYBU94yJfa72v1vq5wf2Hk8wnuTjJryS5ffCy25O8doUZAQCAJ1mVNfKllMuS/FiSzya5sNZ63+Cp7yS5cJn3XFtK2VtK2fvAAw+sRgwAAFg3VlzkSylbkvxRkjfXWv/fY5+rtdYk9UTvq7W+r9a6tda69YILLlhpDAAAWFdWVORLKcNZKvEfrLX+8WD4/lLK8wfPPz/Jd1cWEQAAeLKVnLWmJJlOMl9r/T+PeeqjSa4a3L8qyUeeeTwAAOBENq7gvT+V5DeS3F1K+evB2DuSvCvJH5RSJpLck+TXVpQQAAB4imdc5Gutc0nKMk9f8Uy3CwDQistuvKPrCGvSc5413HWEdWElM/IAAOvWN9716q4jHHXZjXesqTycGaty+kkAAODMUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANcmXXs5DLRS/PJaMBgLOFIn+WWWuXZ3bJaPj++AN8ef4ABzgxRR6gY2vtj11/gAO0wRp5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAANUuQBAKBBijwAADRIkQcAgAYp8gAA0CBFHgAAGqTIAwBAgxR5AABokCIPAAAN2th1ANamUsrqbWvH6myn1ro6GwIAOAso8pyQ0gwAsLZZWgMAAA1S5AEAoEGKPAAANEiRBwCABinyAADQIEUeAAAapMgDAECDFHkAAGiQIg8AAA1S5AEAoEGKPAAANEiRBwCABinyAADQIEUeAAAapMgDAECDFHkAAGiQIg8AAA1S5AEAoEGKPAAANEiRBwCABinyAADQIEUeAAAapMgDAECDFHkAAGiQIg8AAA1S5AEAoEGKPAAANEiRBwCABinyAADQIEUeAAAatLHrAAAA61UpZfW2tWN1tlNrXZ0Ncdqdlhn5UsovlFK+XEr5WinlxtPxGQAArau1rrkb7Vj1Il9KGUryL5P8YpKXJNlWSnnJan8OAMB6V0p5yo3143TMyP9Ekq/VWr9ea30syYeT/Mpp+BwAgHXridI+PDycubm5DA8PHzfO2e90rJG/OMm3jnl8IMnLT8PnAACsa8PDw3nssceSJI899ljOOeecLC4udpyKM6Wzg11LKdcmuTZJXvjCF3YVA+Cs4aA5WH9mZ2ef8nhsbKyjNJxpp6PIfzvJC455fMlg7Di11vcleV+SbN261U96gBVSmmH9GR8fPzoj/8Rj1o/TsUb+r5K8uJTyolLKOUl+PclHT8PnAACsa4uLiznnnHPy53/+55bVrEOrPiNfa328lPI/J/lkkqEku2utX1ztzwEAWM9qrSmlZHFx8bjlNP51bv04LWvka60fT/Lx07FtAACWKO3r22m5IBQAAHB6KfIAANAgRR4AABqkyAMAQIMUeQAAaJAiDwAADVLkAQCgQYo8AAA0SJEHAIAGKfIAANAgRR4AABqkyAMAQIMUeQAAaJAiDwAADVLkAQCgQYo8AAA0SJEHAIAGKfIAANAgRR4AABpUaq1dZ0gp5YEk93Sdg9PieUn+Y9chgKfFfgvtsd+evS6ttV5woifWRJHn7FVK2Vtr3dp1DuD7Z7+F9thv1ydLawAAoEGKPAAANEiR53R7X9cBgKfNfgvtsd+uQ9bIAwBAg8zIAwBAgxR5AABokCJ/liqlvLmU8uyn+7pSysdLKeed1nAnz3NZKeV1XX0+rGWt7tdPVkq5qJTyh13ngNW2VvbRUsoHSim/ukrb+kYp5XmrsS1WnyJ/9npzklP+MHny62qtv1RrffD0RPq+XJZkVYt8WeL/65wN3pw29+ujSikba6331lpXpWTAGvPmNL6P0hbl5ixQStlcSrmjlPKFUsq+UspvJ7koyWwpZXbwmveWUvaWUr5YSnnnYOwfneB1R//yLqW8ZbC9faWUNw/GLiulzJdSbhts609KKc86SbZrSil/Ncj2R0/MQAxmC363lPIXpZSvHzNz8K4kP11K+etSyj8efF8/MnjP50spvzW4/7+XUq4Z3P9fBp/xN8d8b5eVUr5cSvn9JPuSvKCU8kgp5XcGue8spZzwKmmwFqzx/fozpZRbB/vpvlLKTwzGf6KU8peDffUvSimXD8bfUEr5aCnl00nuHHzevsFzLy2l/PvBtv6mlPLi0/NfFFbXWt5Hn5TzisE+eXcpZXcpZdMxn/nOUsrnBs/98GD8Bwfb/2Ip5f1JyjHbWtVsrIJaq1vjtyT/bZLbjnn8nCTfSPK8Y8bOH3wdSvKZJD8yePzk130jS5d5/q+S3J1kc5ItSb6Y5MeyNGP+eJL/cvD6P0jyD0+S7QePuf9PkkwO7n8gyb/J0h+TL0nytcH4zyb52DHvuTHJbw6+p79K8snB+GySy5O8Mkun3CqDbX0syc8Mch5J8pPHbKsmef3g/m8l+Rdd/2/n5rbcbY3v1595Ittgf9s3uP8DSTYO7v9ckj8a3H9DkgPH5L3smPfsPGa/PCfJs7r+b+/m9v3c1vg++oEkv5pkJMm3kvwXg/HfT/LmYz7zid/J/1OS9w/u/26S3xrcf/Xgd+eqZXNb3ZsZ+bPD3Ul+vpSyo5Ty07XWh07wml8rpXwuyeeTvDRL5flkxpL8X7XWhVrrI0n+OMlPD57bX2v968H9u7K0Ey9ntJTyZ6WUu5O8fvDZT/i/a61Haq1fSnLhMu//sywVhZ9KckeSLYNZ/RfVWr+cpSL/ysH39bkkP5zkiRm9e2qt/+6YbR1Jsmdw/18PvkdYq9byfp0kM0lSa/3TJD9Qltb3PifJvxnMtv9Ojt/fP1Vr/bsTbOcvk7yjlLI9yaW11u+d4nNhrVjr+2iyNOG1v9b6lcHj27P0O/UJf3yC7f1Mln5HptZ6R5KDpykbq2Bj1wFYuVrrV0opP57kl5L8k1LKncc+X0p5UZK3Jfmva60HSykfyNJf6c/Uo8fcP5zkZP+E9oEkr621fqGU8oYszbifaDslJ/ZXSbYm+XqST2VpVuCaLP2geOJ9N9daf+/YN5VSLkuycJJcydIsA6xJa3y/Tp66/9Qk/0eS2VrrPxjsg5855vkT7o+11g+VUj6bpZm/j5dS/sda66efTnDoQgP76NPZ5uGsrBOejmx8H8zInwVKKRcl+U+11n+d5J8m+fEkDyc5d/CSH8jSL9GHSikXJvnFY95+7OuO9WdJXltKeXYpZXOSfzAYe7rOTXJfKWU4SzPyp3JcnlrrY1n6Z8H/Lkszd3+WpR+Mfzp4ySeTXF1K2ZIkpZSLSyn/2TLb3pClf2pMlg6onXt63wqcOWt8v06SKwc5x5I8NJiNfE6Sbw+ef8P3s5FSyn+e5Ou11t9N8pEkP/IM88AZ1cA+miRfTnJZKeWHBo9/I8n/c4r3/GkGJ50opfxikueepmysAjPyZ4eXJfmnpZQjSRaTXJ/k7yX5t6WUe2ut46WUzyf5D1kqxX9+zHvfd+zrnhistX5uMHvw7wdD76+1fn4wy/Z0/G9JPpvkgcHXE/3gOtbfJDlcSvlCkg/UWn8nSz8orqi1fq+U8mdJLhmMpdb6J6WUXpK/LKUkySNJ/mGWZgSebCHJT5RS/tck382giMAatZb36yQ5NPj84SRXD8ZuSXL7YB+74/vczq8l+Y1SymKS7yS56RlkgS6s9X00tdZDpZQ3ZmnJ28Ys/Sv3rlO87Z1JZkopX0zyF0m+eTqysTpKrVYXsD6UUh6ptW7pOge0rpTymSRvq7Xu7ToLwHpmaQ0AADTI0hpWRSnlX2bpzDLHurXW+q+6yHMiZuPh6TnJfv2zHcQBnqSF372cXpbWAABAgyytAQCABinyAADQIEUeAAAapMgDAECDFHkAAGjQ/wfOj68MmJAXXAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 921.6x691.2 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# quick visual check\n",
"plt.figure()\n",
"air_quality.plot(figsize=(12.8, 9.6))\n",
"\n",
"\n",
"# plot only paris\n",
"plt.figure()\n",
"air_quality[\"station_paris\"].plot(figsize=(12.8, 9.6))\n",
"\n",
"# box plot\n",
"plt.figure(figsize=(12.8, 9.6))\n",
"air_quality.plot.box(figsize=(12.8, 9.6))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<AxesSubplot:xlabel='datetime'>, <AxesSubplot:xlabel='datetime'>,\n",
" <AxesSubplot:xlabel='datetime'>], dtype=object)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# area plot\n",
"plt.figure()\n",
"air_quality.plot.area(figsize=(12, 4), subplots=True)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>station_antwerp</th>\n",
" <th>station_paris</th>\n",
" <th>station_london</th>\n",
" <th>london_mg_per_cubic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-05-07 02:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" <td>43.286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 03:00:00</th>\n",
" <td>50.5</td>\n",
" <td>25.0</td>\n",
" <td>19.0</td>\n",
" <td>35.758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 04:00:00</th>\n",
" <td>45.0</td>\n",
" <td>27.7</td>\n",
" <td>19.0</td>\n",
" <td>35.758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 05:00:00</th>\n",
" <td>NaN</td>\n",
" <td>50.4</td>\n",
" <td>16.0</td>\n",
" <td>30.112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 06:00:00</th>\n",
" <td>NaN</td>\n",
" <td>61.9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" station_antwerp station_paris station_london \\\n",
"datetime \n",
"2019-05-07 02:00:00 NaN NaN 23.0 \n",
"2019-05-07 03:00:00 50.5 25.0 19.0 \n",
"2019-05-07 04:00:00 45.0 27.7 19.0 \n",
"2019-05-07 05:00:00 NaN 50.4 16.0 \n",
"2019-05-07 06:00:00 NaN 61.9 NaN \n",
"\n",
" london_mg_per_cubic \n",
"datetime \n",
"2019-05-07 02:00:00 43.286 \n",
"2019-05-07 03:00:00 35.758 \n",
"2019-05-07 04:00:00 35.758 \n",
"2019-05-07 05:00:00 30.112 \n",
"2019-05-07 06:00:00 NaN "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create new columns derived from existing columns\n",
"air_quality[\"london_mg_per_cubic\"] = air_quality[\"station_london\"] * 1.882\n",
"air_quality.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>station_antwerp</th>\n",
" <th>station_paris</th>\n",
" <th>station_london</th>\n",
" <th>london_mpc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-05-07 02:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" <td>43.286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 03:00:00</th>\n",
" <td>50.5</td>\n",
" <td>25.0</td>\n",
" <td>19.0</td>\n",
" <td>35.758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 04:00:00</th>\n",
" <td>45.0</td>\n",
" <td>27.7</td>\n",
" <td>19.0</td>\n",
" <td>35.758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 05:00:00</th>\n",
" <td>NaN</td>\n",
" <td>50.4</td>\n",
" <td>16.0</td>\n",
" <td>30.112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 06:00:00</th>\n",
" <td>NaN</td>\n",
" <td>61.9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" station_antwerp station_paris station_london \\\n",
"datetime \n",
"2019-05-07 02:00:00 NaN NaN 23.0 \n",
"2019-05-07 03:00:00 50.5 25.0 19.0 \n",
"2019-05-07 04:00:00 45.0 27.7 19.0 \n",
"2019-05-07 05:00:00 NaN 50.4 16.0 \n",
"2019-05-07 06:00:00 NaN 61.9 NaN \n",
"\n",
" london_mpc \n",
"datetime \n",
"2019-05-07 02:00:00 43.286 \n",
"2019-05-07 03:00:00 35.758 \n",
"2019-05-07 04:00:00 35.758 \n",
"2019-05-07 05:00:00 30.112 \n",
"2019-05-07 06:00:00 NaN "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rename column\n",
"air_quality_renamed = air_quality.rename(columns={'london_mg_per_cubic': 'london_mpc'}) # columns=str.lower\n",
"air_quality_renamed.head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>station_antwerp</th>\n",
" <th>station_paris</th>\n",
" <th>station_london</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-05-07 02:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 03:00:00</th>\n",
" <td>50.5</td>\n",
" <td>25.0</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 04:00:00</th>\n",
" <td>45.0</td>\n",
" <td>27.7</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 05:00:00</th>\n",
" <td>NaN</td>\n",
" <td>50.4</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-07 06:00:00</th>\n",
" <td>NaN</td>\n",
" <td>61.9</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" station_antwerp station_paris station_london\n",
"datetime \n",
"2019-05-07 02:00:00 NaN NaN 23.0\n",
"2019-05-07 03:00:00 50.5 25.0 19.0\n",
"2019-05-07 04:00:00 45.0 27.7 19.0\n",
"2019-05-07 05:00:00 NaN 50.4 16.0\n",
"2019-05-07 06:00:00 NaN 61.9 NaN"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# delete column\n",
"del air_quality['london_mg_per_cubic']\n",
"air_quality.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age mean: \n",
"29.69911764705882\n",
"\n",
"age/fare medians: \n",
"Age 28.0000\n",
"Fare 14.4542\n",
"dtype: float64\n",
"\n",
"age/fare describe\n",
" Age Fare\n",
"count 714.000000 891.000000\n",
"mean 29.699118 32.204208\n",
"std 14.526497 49.693429\n",
"min 0.420000 0.000000\n",
"25% 20.125000 7.910400\n",
"50% 28.000000 14.454200\n",
"75% 38.000000 31.000000\n",
"max 80.000000 512.329200\n",
"\n",
"customize aggregation\n",
" Age Fare\n",
"max 80.000000 512.329200\n",
"mean NaN 32.204208\n",
"median 28.000000 14.454200\n",
"min 0.420000 0.000000\n",
"skew 0.389108 NaN\n"
]
}
],
"source": [
"# aggregating statistics\n",
"print('age mean: ')\n",
"print(titanic[\"Age\"].mean())\n",
"print()\n",
"print('age/fare medians: ')\n",
"print(titanic[[\"Age\", \"Fare\"]].median())\n",
"print()\n",
"print('age/fare describe')\n",
"print(titanic[[\"Age\", \"Fare\"]].describe())\n",
"print()\n",
"print('customize aggregation')\n",
"# skew: negative skewness means left tailed, aka mass of distribution is concentrated on te right\n",
"print(titanic.agg({'Age': ['min', 'max', 'median', 'skew'],\n",
" 'Fare': ['min', 'max', 'median', 'mean']}))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"titanic[[\"Sex\", \"Age\"]].groupby(\"Sex\").mean()\n",
" Age\n",
"Sex \n",
"female 27.915709\n",
"male 30.726645\n",
"\n",
"titanic.groupby([\"Sex\", \"Pclass\"])[\"Fare\"].mean()\n",
"Sex Pclass\n",
"female 1 106.125798\n",
" 2 21.970121\n",
" 3 16.118810\n",
"male 1 67.226127\n",
" 2 19.741782\n",
" 3 12.661633\n",
"Name: Fare, dtype: float64\n",
"\n",
"titanic[\"Pclass\"].value_counts() shortcut to titanic.groupby(\"Pclass\")[\"Pclass\"].count()\n"
]
},
{
"data": {
"text/plain": [
"3 491\n",
"1 216\n",
"2 184\n",
"Name: Pclass, dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculat average age for male vs female\n",
"print('titanic[[\"Sex\", \"Age\"]].groupby(\"Sex\").mean()')\n",
"print(titanic[[\"Sex\", \"Age\"]].groupby(\"Sex\").mean())\n",
"print()\n",
"\n",
"print('titanic.groupby([\"Sex\", \"Pclass\"])[\"Fare\"].mean()')\n",
"print(titanic.groupby([\"Sex\", \"Pclass\"])[\"Fare\"].mean())\n",
"print()\n",
"\n",
"# `size` includes NaN values while `count` excludes them\n",
"print('titanic[\"Pclass\"].value_counts() shortcut to titanic.groupby(\"Pclass\")[\"Pclass\"].count()')\n",
"titanic[\"Pclass\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>anonymous</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>anonymous</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>anonymous</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp Parch \\\n",
"0 anonymous male 22.0 1 0 \n",
"1 anonymous female 38.0 1 0 \n",
"2 anonymous female 26.0 0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 \n",
"4 Allen, Mr. William Henry male 35.0 0 0 \n",
"\n",
" Ticket Fare Cabin Embarked \n",
"0 A/5 21171 7.2500 NaN S \n",
"1 PC 17599 71.2833 C85 C \n",
"2 STON/O2. 3101282 7.9250 NaN S \n",
"3 113803 53.1000 C123 S \n",
"4 373450 8.0500 NaN S "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>851</th>\n",
" <td>852</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Svensson, Mr. Johan</td>\n",
" <td>male</td>\n",
" <td>74.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>347060</td>\n",
" <td>7.7750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>117</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Connors, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>70.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370369</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>281</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Duane, Mr. Frank</td>\n",
" <td>male</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>336439</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>483</th>\n",
" <td>484</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Turkula, Mrs. (Hedwig)</td>\n",
" <td>female</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4134</td>\n",
" <td>9.5875</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>326</th>\n",
" <td>327</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Nysveen, Mr. Johan Hansen</td>\n",
" <td>male</td>\n",
" <td>61.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345364</td>\n",
" <td>6.2375</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Name Sex Age \\\n",
"851 852 0 3 Svensson, Mr. Johan male 74.0 \n",
"116 117 0 3 Connors, Mr. Patrick male 70.5 \n",
"280 281 0 3 Duane, Mr. Frank male 65.0 \n",
"483 484 1 3 Turkula, Mrs. (Hedwig) female 63.0 \n",
"326 327 0 3 Nysveen, Mr. Johan Hansen male 61.0 \n",
"\n",
" SibSp Parch Ticket Fare Cabin Embarked \n",
"851 0 0 347060 7.7750 NaN S \n",
"116 0 0 370369 7.7500 NaN Q \n",
"280 0 0 336439 7.7500 NaN Q \n",
"483 0 0 4134 9.5875 NaN S \n",
"326 0 0 345364 6.2375 NaN S "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sorting\n",
"titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"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>city</th>\n",
" <th>country</th>\n",
" <th>location</th>\n",
" <th>parameter</th>\n",
" <th>value</th>\n",
" <th>unit</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date.utc</th>\n",
" <th></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>2019-06-18 06:00:00+00:00</th>\n",
" <td>Antwerpen</td>\n",
" <td>BE</td>\n",
" <td>BETR801</td>\n",
" <td>pm25</td>\n",
" <td>18.0</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17 08:00:00+00:00</th>\n",
" <td>Antwerpen</td>\n",
" <td>BE</td>\n",
" <td>BETR801</td>\n",
" <td>pm25</td>\n",
" <td>6.5</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17 07:00:00+00:00</th>\n",
" <td>Antwerpen</td>\n",
" <td>BE</td>\n",
" <td>BETR801</td>\n",
" <td>pm25</td>\n",
" <td>18.5</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17 06:00:00+00:00</th>\n",
" <td>Antwerpen</td>\n",
" <td>BE</td>\n",
" <td>BETR801</td>\n",
" <td>pm25</td>\n",
" <td>16.0</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17 05:00:00+00:00</th>\n",
" <td>Antwerpen</td>\n",
" <td>BE</td>\n",
" <td>BETR801</td>\n",
" <td>pm25</td>\n",
" <td>7.5</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city country location parameter value unit\n",
"date.utc \n",
"2019-06-18 06:00:00+00:00 Antwerpen BE BETR801 pm25 18.0 µg/m³\n",
"2019-06-17 08:00:00+00:00 Antwerpen BE BETR801 pm25 6.5 µg/m³\n",
"2019-06-17 07:00:00+00:00 Antwerpen BE BETR801 pm25 18.5 µg/m³\n",
"2019-06-17 06:00:00+00:00 Antwerpen BE BETR801 pm25 16.0 µg/m³\n",
"2019-06-17 05:00:00+00:00 Antwerpen BE BETR801 pm25 7.5 µg/m³"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_quality_long = pd.read_csv(\"data/air_quality_long.csv\", index_col='date.utc', parse_dates=True)\n",
"air_quality_long.head()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FR04014 1676\n",
"London Westminster 1608\n",
"BETR801 163\n",
"Name: location, dtype: int64\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>city</th>\n",
" <th>country</th>\n",
" <th>location</th>\n",
" <th>parameter</th>\n",
" <th>value</th>\n",
" <th>unit</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date.utc</th>\n",
" <th></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>2019-06-21 00:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>20.0</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 23:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>21.8</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 22:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>26.5</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 21:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>24.9</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 20:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>21.4</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city country location parameter value unit\n",
"date.utc \n",
"2019-06-21 00:00:00+00:00 Paris FR FR04014 no2 20.0 µg/m³\n",
"2019-06-20 23:00:00+00:00 Paris FR FR04014 no2 21.8 µg/m³\n",
"2019-06-20 22:00:00+00:00 Paris FR FR04014 no2 26.5 µg/m³\n",
"2019-06-20 21:00:00+00:00 Paris FR FR04014 no2 24.9 µg/m³\n",
"2019-06-20 20:00:00+00:00 Paris FR FR04014 no2 21.4 µg/m³"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pivot table\n",
"no2 = air_quality_long[air_quality_long[\"parameter\"] == 'no2']\n",
"print(no2[\"location\"].value_counts())\n",
"no2.head()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>city</th>\n",
" <th>country</th>\n",
" <th>location</th>\n",
" <th>parameter</th>\n",
" <th>value</th>\n",
" <th>unit</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date.utc</th>\n",
" <th></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>2019-04-09 01:00:00+00:00</th>\n",
" <td>Antwerpen</td>\n",
" <td>BE</td>\n",
" <td>BETR801</td>\n",
" <td>no2</td>\n",
" <td>22.5</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-09 01:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>24.4</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-09 02:00:00+00:00</th>\n",
" <td>London</td>\n",
" <td>GB</td>\n",
" <td>London Westminster</td>\n",
" <td>no2</td>\n",
" <td>67.0</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-09 02:00:00+00:00</th>\n",
" <td>Antwerpen</td>\n",
" <td>BE</td>\n",
" <td>BETR801</td>\n",
" <td>no2</td>\n",
" <td>53.5</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-09 02:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>27.4</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-09 03:00:00+00:00</th>\n",
" <td>London</td>\n",
" <td>GB</td>\n",
" <td>London Westminster</td>\n",
" <td>no2</td>\n",
" <td>67.0</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city country location parameter \\\n",
"date.utc \n",
"2019-04-09 01:00:00+00:00 Antwerpen BE BETR801 no2 \n",
"2019-04-09 01:00:00+00:00 Paris FR FR04014 no2 \n",
"2019-04-09 02:00:00+00:00 London GB London Westminster no2 \n",
"2019-04-09 02:00:00+00:00 Antwerpen BE BETR801 no2 \n",
"2019-04-09 02:00:00+00:00 Paris FR FR04014 no2 \n",
"2019-04-09 03:00:00+00:00 London GB London Westminster no2 \n",
"\n",
" value unit \n",
"date.utc \n",
"2019-04-09 01:00:00+00:00 22.5 µg/m³ \n",
"2019-04-09 01:00:00+00:00 24.4 µg/m³ \n",
"2019-04-09 02:00:00+00:00 67.0 µg/m³ \n",
"2019-04-09 02:00:00+00:00 53.5 µg/m³ \n",
"2019-04-09 02:00:00+00:00 27.4 µg/m³ \n",
"2019-04-09 03:00:00+00:00 67.0 µg/m³ "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# use 2 measurements (head) for each location (groupby)\n",
"no2_subset = no2.sort_index().groupby('location').head(2)\n",
"no2_subset"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>location</th>\n",
" <th>BETR801</th>\n",
" <th>FR04014</th>\n",
" <th>London Westminster</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date.utc</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-09 01:00:00+00:00</th>\n",
" <td>22.5</td>\n",
" <td>24.4</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-09 02:00:00+00:00</th>\n",
" <td>53.5</td>\n",
" <td>27.4</td>\n",
" <td>67.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-09 03:00:00+00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>67.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"location BETR801 FR04014 London Westminster\n",
"date.utc \n",
"2019-04-09 01:00:00+00:00 22.5 24.4 NaN\n",
"2019-04-09 02:00:00+00:00 53.5 27.4 67.0\n",
"2019-04-09 03:00:00+00:00 NaN NaN 67.0"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pivot table\n",
"no2_subset.pivot(columns=\"location\", values=\"value\")"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='date.utc'>"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"no2.pivot(columns=\"location\", values=\"value\").plot(figsize=(12,4))"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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>parameter</th>\n",
" <th>no2</th>\n",
" <th>pm25</th>\n",
" </tr>\n",
" <tr>\n",
" <th>location</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BETR801</th>\n",
" <td>26.950920</td>\n",
" <td>23.169492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FR04014</th>\n",
" <td>29.374284</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>London Westminster</th>\n",
" <td>29.740050</td>\n",
" <td>13.443568</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"parameter no2 pm25\n",
"location \n",
"BETR801 26.950920 23.169492\n",
"FR04014 29.374284 NaN\n",
"London Westminster 29.740050 13.443568"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pivot table\n",
"air_quality_long.pivot_table(\n",
" values=\"value\",\n",
" index=\"location\",\n",
" columns=[\"parameter\"],\n",
" aggfunc=\"mean\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" city country date.utc location parameter value unit\n",
"0 Paris FR 2019-06-21 00:00:00+00:00 FR04014 no2 20.0 µg/m³\n",
"1 Paris FR 2019-06-20 23:00:00+00:00 FR04014 no2 21.8 µg/m³\n",
"2 Paris FR 2019-06-20 22:00:00+00:00 FR04014 no2 26.5 µg/m³\n",
"3 Paris FR 2019-06-20 21:00:00+00:00 FR04014 no2 24.9 µg/m³\n",
"4 Paris FR 2019-06-20 20:00:00+00:00 FR04014 no2 21.4 µg/m³\n",
" city country date.utc location parameter value \\\n",
"0 Antwerpen BE 2019-06-18 06:00:00+00:00 BETR801 pm25 18.0 \n",
"1 Antwerpen BE 2019-06-17 08:00:00+00:00 BETR801 pm25 6.5 \n",
"2 Antwerpen BE 2019-06-17 07:00:00+00:00 BETR801 pm25 18.5 \n",
"3 Antwerpen BE 2019-06-17 06:00:00+00:00 BETR801 pm25 16.0 \n",
"4 Antwerpen BE 2019-06-17 05:00:00+00:00 BETR801 pm25 7.5 \n",
"\n",
" unit \n",
"0 µg/m³ \n",
"1 µg/m³ \n",
"2 µg/m³ \n",
"3 µg/m³ \n",
"4 µg/m³ \n"
]
}
],
"source": [
"# combine data from multiple dataframe, concatenation\n",
"\n",
"air_quality_no2_long = pd.read_csv('data/air_quality_no2_long.csv', parse_dates=[\"date.utc\"])\n",
"air_quality_pm25_long = pd.read_csv('data/air_quality_pm25_long.csv', parse_dates=[\"date.utc\"])\n",
"\n",
"print(air_quality_no2_long.head())\n",
"print(air_quality_pm25_long.head())"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" city country date.utc location parameter value unit\n",
"0 Paris FR 2019-06-21 00:00:00+00:00 FR04014 no2 20.0 µg/m³\n",
"1 Paris FR 2019-06-20 23:00:00+00:00 FR04014 no2 21.8 µg/m³\n",
"2 Paris FR 2019-06-20 22:00:00+00:00 FR04014 no2 26.5 µg/m³\n",
"3 Paris FR 2019-06-20 21:00:00+00:00 FR04014 no2 24.9 µg/m³\n",
"4 Paris FR 2019-06-20 20:00:00+00:00 FR04014 no2 21.4 µg/m³\n",
" city country date.utc location parameter \\\n",
"1105 London GB 2019-05-07 06:00:00+00:00 London Westminster pm25 \n",
"1106 London GB 2019-05-07 04:00:00+00:00 London Westminster pm25 \n",
"1107 London GB 2019-05-07 03:00:00+00:00 London Westminster pm25 \n",
"1108 London GB 2019-05-07 02:00:00+00:00 London Westminster pm25 \n",
"1109 London GB 2019-05-07 01:00:00+00:00 London Westminster pm25 \n",
"\n",
" value unit \n",
"1105 9.0 µg/m³ \n",
"1106 8.0 µg/m³ \n",
"1107 8.0 µg/m³ \n",
"1108 8.0 µg/m³ \n",
"1109 8.0 µg/m³ \n"
]
}
],
"source": [
"# combine by rows\n",
"air_quality_all = pd.concat([air_quality_no2_long, air_quality_pm25_long], axis=0)\n",
"print(air_quality_all.head())\n",
"print(air_quality_all.tail())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# more combination methods\n",
"https://pandas.pydata.org/docs/getting_started/intro_tutorasdfials/08_combine_dataframes.html"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Paris', 'Antwerpen', 'London'], dtype=object)"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_quality_all.city.unique()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Paris', 'Antwerpen', 'London'], dtype=object)"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_quality_all['city'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"air_quality_all = air_quality_all.rename(columns={'date.utc': 'datetime'})\n",
"air_quality_all.datetime = pd.to_datetime(air_quality_all.datetime) # convert to pd.Timestamp"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2019-06-21 00:00:00+00:00\n",
"1 2019-06-20 23:00:00+00:00\n",
"2 2019-06-20 22:00:00+00:00\n",
"3 2019-06-20 21:00:00+00:00\n",
"4 2019-06-20 20:00:00+00:00\n",
" ... \n",
"1105 2019-05-07 06:00:00+00:00\n",
"1106 2019-05-07 04:00:00+00:00\n",
"1107 2019-05-07 03:00:00+00:00\n",
"1108 2019-05-07 02:00:00+00:00\n",
"1109 2019-05-07 01:00:00+00:00\n",
"Name: datetime, Length: 3178, dtype: datetime64[ns, UTC]"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_quality_all.datetime"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2019-06-21 00:00:00+00:00\n",
"1 2019-06-20 23:00:00+00:00\n",
"2 2019-06-20 22:00:00+00:00\n",
"3 2019-06-20 21:00:00+00:00\n",
"4 2019-06-20 20:00:00+00:00\n",
" ... \n",
"2063 2019-05-07 06:00:00+00:00\n",
"2064 2019-05-07 04:00:00+00:00\n",
"2065 2019-05-07 03:00:00+00:00\n",
"2066 2019-05-07 02:00:00+00:00\n",
"2067 2019-05-07 01:00:00+00:00\n",
"Name: date.utc, Length: 2068, dtype: datetime64[ns, UTC]"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air_quality_no2_long['date.utc']"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timedelta('44 days 23:00:00')"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculates timestamp delta\n",
"air_quality_all.datetime.max() - air_quality_all.datetime.min() # returns pd.Timedelta"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"date.utc location \n",
"0 BETR801 27.875000\n",
" FR04014 24.856250\n",
" London Westminster 23.969697\n",
"1 BETR801 22.214286\n",
" FR04014 30.999359\n",
" London Westminster 24.885714\n",
"2 BETR801 21.125000\n",
" FR04014 29.165753\n",
" London Westminster 23.460432\n",
"3 BETR801 27.500000\n",
" FR04014 28.600690\n",
" London Westminster 24.780142\n",
"4 BETR801 28.400000\n",
" FR04014 31.617986\n",
" London Westminster 26.446809\n",
"5 BETR801 33.500000\n",
" FR04014 25.266154\n",
" London Westminster 24.977612\n",
"6 BETR801 21.896552\n",
" FR04014 23.274306\n",
" London Westminster 24.859155\n",
"Name: value, dtype: float64"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculates average NO2 concentrationfor each day of week \n",
"# for each of the measurement locations\n",
"\n",
"air_quality_no2_long.groupby([\n",
" air_quality_no2_long[\"date.utc\"].dt.weekday,\n",
" \"location\"\n",
"])[\"value\"].mean()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot the typical NO2 pattern during the day of our time series\n",
"# of all stations together. that is the average value for each hour of day\n",
"fig, axs = plt.subplots(figsize=(12, 4))\n",
"g = air_quality_no2_long.groupby([air_quality_no2_long[\"date.utc\"].dt.hour])\n",
"g[\"value\"].mean().plot(kind=\"bar\", rot=0, ax=axs)\n",
"plt.xlabel(\"Hour of the day\")\n",
"plt.ylabel(\"$NO_2 (\\mu g/m^3)$\");"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 2068 entries, 0 to 2067\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 city 2068 non-null object \n",
" 1 country 2068 non-null object \n",
" 2 date.utc 2068 non-null datetime64[ns, UTC]\n",
" 3 location 2068 non-null object \n",
" 4 parameter 2068 non-null object \n",
" 5 value 2068 non-null float64 \n",
" 6 unit 2068 non-null object \n",
"dtypes: datetime64[ns, UTC](1), float64(1), object(5)\n",
"memory usage: 113.2+ KB\n"
]
}
],
"source": [
"air_quality_no2_long.info()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 2068 entries, 2019-06-21 00:00:00+00:00 to 2019-05-07 01:00:00+00:00\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 city 2068 non-null object \n",
" 1 country 2068 non-null object \n",
" 2 location 2068 non-null object \n",
" 3 parameter 2068 non-null object \n",
" 4 value 2068 non-null float64\n",
" 5 unit 2068 non-null object \n",
"dtypes: float64(1), object(5)\n",
"memory usage: 113.1+ KB\n",
"None\n",
"Int64Index([2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,\n",
" ...\n",
" 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019],\n",
" dtype='int64', name='date.utc', length=2068)\n"
]
},
{
"data": {
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"<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>city</th>\n",
" <th>country</th>\n",
" <th>location</th>\n",
" <th>parameter</th>\n",
" <th>value</th>\n",
" <th>unit</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date.utc</th>\n",
" <th></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>2019-06-21 00:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>20.0</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 23:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>21.8</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 22:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>26.5</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 21:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>24.9</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-20 20:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>FR</td>\n",
" <td>FR04014</td>\n",
" <td>no2</td>\n",
" <td>21.4</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city country location parameter value unit\n",
"date.utc \n",
"2019-06-21 00:00:00+00:00 Paris FR FR04014 no2 20.0 µg/m³\n",
"2019-06-20 23:00:00+00:00 Paris FR FR04014 no2 21.8 µg/m³\n",
"2019-06-20 22:00:00+00:00 Paris FR FR04014 no2 26.5 µg/m³\n",
"2019-06-20 21:00:00+00:00 Paris FR FR04014 no2 24.9 µg/m³\n",
"2019-06-20 20:00:00+00:00 Paris FR FR04014 no2 21.4 µg/m³"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# use date.utc as index by using DataFrame.set_index method\n",
"no2_long = air_quality_no2_long.set_index('date.utc')\n",
"print(no2_long.info())\n",
"print(no2_long.index.year)\n",
"no2_long.head()"
]
},
{
"cell_type": "code",
"execution_count": 130,
"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>city</th>\n",
" <th>country</th>\n",
" <th>location</th>\n",
" <th>parameter</th>\n",
" <th>value</th>\n",
" <th>unit</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date.utc</th>\n",
" <th></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>2019-05-31 00:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>GB</td>\n",
" <td>London Westminster</td>\n",
" <td>no2</td>\n",
" <td>97.0</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-30 00:00:00+00:00</th>\n",
" <td>Paris</td>\n",
" <td>GB</td>\n",
" <td>London Westminster</td>\n",
" <td>no2</td>\n",
" <td>84.7</td>\n",
" <td>µg/m³</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city country location parameter value \\\n",
"date.utc \n",
"2019-05-31 00:00:00+00:00 Paris GB London Westminster no2 97.0 \n",
"2019-06-30 00:00:00+00:00 Paris GB London Westminster no2 84.7 \n",
"\n",
" unit \n",
"date.utc \n",
"2019-05-31 00:00:00+00:00 µg/m³ \n",
"2019-06-30 00:00:00+00:00 µg/m³ "
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# aggregate the current hourly time series values to the monthly maximum value \n",
"# resample provides a time-based grouping: M,5H..., requires an aggregation funciton\n",
"no2_long.resample('M').max()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 anonymous\n",
"1 anonymous\n",
"2 anonymous\n",
"3 futrelle, mrs. jacques heath (lily may peel)\n",
"4 allen, mr. william henry\n",
" ... \n",
"886 montvila, rev. juozas\n",
"887 graham, miss. margaret edith\n",
"888 johnston, miss. catherine helen \"carrie\"\n",
"889 behr, mr. karl howell\n",
"890 dooley, mr. patrick\n",
"Name: Name, Length: 891, dtype: object"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# manipulate textual data\n",
"\n",
"titanic[\"Name\"].str.lower()"
]
},
{
"cell_type": "code",
"execution_count": 136,
"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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Surname</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>anonymous</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>anonymous</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>anonymous</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>anonymous</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>anonymous</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>anonymous</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>Futrelle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>Allen</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp Parch \\\n",
"0 anonymous male 22.0 1 0 \n",
"1 anonymous female 38.0 1 0 \n",
"2 anonymous female 26.0 0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 \n",
"4 Allen, Mr. William Henry male 35.0 0 0 \n",
"\n",
" Ticket Fare Cabin Embarked Surname \n",
"0 A/5 21171 7.2500 NaN S anonymous \n",
"1 PC 17599 71.2833 C85 C anonymous \n",
"2 STON/O2. 3101282 7.9250 NaN S anonymous \n",
"3 113803 53.1000 C123 S Futrelle \n",
"4 373450 8.0500 NaN S Allen "
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a Surname from Name column\n",
"titanic[\"Surname\"] = titanic[\"Name\"].str.split(\",\").str.get(0)\n",
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Surname</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>759</th>\n",
" <td>760</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Rothes, the Countess. of (Lucy Noel Martha Dye...</td>\n",
" <td>female</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>110152</td>\n",
" <td>86.5</td>\n",
" <td>B77</td>\n",
" <td>S</td>\n",
" <td>Rothes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"759 760 1 1 \n",
"\n",
" Name Sex Age SibSp \\\n",
"759 Rothes, the Countess. of (Lucy Noel Martha Dye... female 33.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Surname \n",
"759 0 110152 86.5 B77 S Rothes "
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# extract the passenger data about the Countesses on board of the Titanic\n",
"titanic[titanic[\"Name\"].str.contains(\"Countess\")]"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)'"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get index of longest name\n",
"titanic[\"Name\"].str.len().idxmax()\n",
"# get longest name of table\n",
"titanic.loc[titanic[\"Name\"].str.len().idxmax(), \"Name\"]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
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