{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"\n",
"# Class 6: DataFrames and visualization\n",
"\n",
"---\n",
"\n",
"\n",
"\n",
"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Load packages\n",
"\n",
"* Note the `%matplotlib inline` statement. This is required if you want to embed images in a Jupyter notebook."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cell_style": "center",
"slideshow": {
"cell_style": "split",
"slide_type": "-"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use the Pandas library to do statistics on tabular data.\n",
"\n",
"* Pandas is a widely-used Python library for statistics, particularly on tabular data.\n",
"* Borrows many features from R's dataframes.\n",
" * A 2-dimenstional table whose columns have names and potentially have different data types.\n",
"* Load it with `import pandas`.\n",
"* Read a Comma Separate Values (CSV) data file with `pandas.read_csv`.\n",
" * Argument is the name of the file to be read.\n",
" * Assign result to a variable to store the data that was read."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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" }\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>country</th>\n",
" <th>gdpPercap_1952</th>\n",
" <th>gdpPercap_1957</th>\n",
" <th>gdpPercap_1962</th>\n",
" <th>gdpPercap_1967</th>\n",
" <th>gdpPercap_1972</th>\n",
" <th>gdpPercap_1977</th>\n",
" <th>gdpPercap_1982</th>\n",
" <th>gdpPercap_1987</th>\n",
" <th>gdpPercap_1992</th>\n",
" <th>gdpPercap_1997</th>\n",
" <th>gdpPercap_2002</th>\n",
" <th>gdpPercap_2007</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Australia</td>\n",
" <td>10039.59564</td>\n",
" <td>10949.64959</td>\n",
" <td>12217.22686</td>\n",
" <td>14526.12465</td>\n",
" <td>16788.62948</td>\n",
" <td>18334.19751</td>\n",
" <td>19477.00928</td>\n",
" <td>21888.88903</td>\n",
" <td>23424.76683</td>\n",
" <td>26997.93657</td>\n",
" <td>30687.75473</td>\n",
" <td>34435.36744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>New Zealand</td>\n",
" <td>10556.57566</td>\n",
" <td>12247.39532</td>\n",
" <td>13175.67800</td>\n",
" <td>14463.91893</td>\n",
" <td>16046.03728</td>\n",
" <td>16233.71770</td>\n",
" <td>17632.41040</td>\n",
" <td>19007.19129</td>\n",
" <td>18363.32494</td>\n",
" <td>21050.41377</td>\n",
" <td>23189.80135</td>\n",
" <td>25185.00911</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 \\\n",
"0 Australia 10039.59564 10949.64959 12217.22686 \n",
"1 New Zealand 10556.57566 12247.39532 13175.67800 \n",
"\n",
" gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 \\\n",
"0 14526.12465 16788.62948 18334.19751 19477.00928 \n",
"1 14463.91893 16046.03728 16233.71770 17632.41040 \n",
"\n",
" gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 \\\n",
"0 21888.88903 23424.76683 26997.93657 30687.75473 \n",
"1 19007.19129 18363.32494 21050.41377 23189.80135 \n",
"\n",
" gdpPercap_2007 \n",
"0 34435.36744 \n",
"1 25185.00911 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('../../data/gapminder/gapminder_gdp_oceania.csv')\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" country gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 \\\n",
"0 Australia 10039.59564 10949.64959 12217.22686 \n",
"1 New Zealand 10556.57566 12247.39532 13175.67800 \n",
"\n",
" gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 \\\n",
"0 14526.12465 16788.62948 18334.19751 19477.00928 \n",
"1 14463.91893 16046.03728 16233.71770 17632.41040 \n",
"\n",
" gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 \\\n",
"0 21888.88903 23424.76683 26997.93657 30687.75473 \n",
"1 19007.19129 18363.32494 21050.41377 23189.80135 \n",
"\n",
" gdpPercap_2007 \n",
"0 34435.36744 \n",
"1 25185.00911 \n"
]
}
],
"source": [
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Note about Pandas DataFrames/Series\n",
"\n",
"A [DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) is a collection of [Series](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html); The DataFrame is the way Pandas represents a table, and Series is the data-structure Pandas use to represent a column.\n",
"\n",
"Pandas is built on top of the [Numpy](http://www.numpy.org/) library, which in practice means that most of the methods defined for Numpy Arrays apply to Pandas Series/DataFrames.\n",
"\n",
"What makes Pandas so attractive is the powerful interface to access individual records of the table, proper handling of missing values, and relational-databases operations between DataFrames."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use `index_col` to specify that a column's values should be used as row headings.\n",
"\n",
"* Row headings are numbers (0 and 1 in this case).\n",
"* Really want to index by country.\n",
"* Pass the name of the column to `read_csv` as its `index_col` parameter to do this."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gdpPercap_1952</th>\n",
" <th>gdpPercap_1957</th>\n",
" <th>gdpPercap_1962</th>\n",
" <th>gdpPercap_1967</th>\n",
" <th>gdpPercap_1972</th>\n",
" <th>gdpPercap_1977</th>\n",
" <th>gdpPercap_1982</th>\n",
" <th>gdpPercap_1987</th>\n",
" <th>gdpPercap_1992</th>\n",
" <th>gdpPercap_1997</th>\n",
" <th>gdpPercap_2002</th>\n",
" <th>gdpPercap_2007</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>Australia</th>\n",
" <td>10039.59564</td>\n",
" <td>10949.64959</td>\n",
" <td>12217.22686</td>\n",
" <td>14526.12465</td>\n",
" <td>16788.62948</td>\n",
" <td>18334.19751</td>\n",
" <td>19477.00928</td>\n",
" <td>21888.88903</td>\n",
" <td>23424.76683</td>\n",
" <td>26997.93657</td>\n",
" <td>30687.75473</td>\n",
" <td>34435.36744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Zealand</th>\n",
" <td>10556.57566</td>\n",
" <td>12247.39532</td>\n",
" <td>13175.67800</td>\n",
" <td>14463.91893</td>\n",
" <td>16046.03728</td>\n",
" <td>16233.71770</td>\n",
" <td>17632.41040</td>\n",
" <td>19007.19129</td>\n",
" <td>18363.32494</td>\n",
" <td>21050.41377</td>\n",
" <td>23189.80135</td>\n",
" <td>25185.00911</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 \\\n",
"country \n",
"Australia 10039.59564 10949.64959 12217.22686 14526.12465 \n",
"New Zealand 10556.57566 12247.39532 13175.67800 14463.91893 \n",
"\n",
" gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 \\\n",
"country \n",
"Australia 16788.62948 18334.19751 19477.00928 21888.88903 \n",
"New Zealand 16046.03728 16233.71770 17632.41040 19007.19129 \n",
"\n",
" gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 \n",
"country \n",
"Australia 23424.76683 26997.93657 30687.75473 34435.36744 \n",
"New Zealand 18363.32494 21050.41377 23189.80135 25185.00911 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('../../data/gapminder/gapminder_gdp_oceania.csv', index_col='country')\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use `DataFrame.info` to find out more about a dataframe.\n",
"\n",
"* This is a `DataFrame`\n",
"* Two rows named `'Australia'` and `'New Zealand'`\n",
"* Twelve columns, each of which has two actual 64-bit floating point values.\n",
"* Uses 208 bytes of memory."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"cell_style": "split",
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 2 entries, Australia to New Zealand\n",
"Data columns (total 12 columns):\n",
"gdpPercap_1952 2 non-null float64\n",
"gdpPercap_1957 2 non-null float64\n",
"gdpPercap_1962 2 non-null float64\n",
"gdpPercap_1967 2 non-null float64\n",
"gdpPercap_1972 2 non-null float64\n",
"gdpPercap_1977 2 non-null float64\n",
"gdpPercap_1982 2 non-null float64\n",
"gdpPercap_1987 2 non-null float64\n",
"gdpPercap_1992 2 non-null float64\n",
"gdpPercap_1997 2 non-null float64\n",
"gdpPercap_2002 2 non-null float64\n",
"gdpPercap_2007 2 non-null float64\n",
"dtypes: float64(12)\n",
"memory usage: 208.0+ bytes\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## The `DataFrame.columns` variable stores information about the dataframe’s columns.\n",
"\n",
"* Note that this is data, not a method.\n",
" * Like `math.pi`.\n",
" * So do not use `()` to try to call it.\n",
"* Called a member variable, or just member."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"cell_style": "split",
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967',\n",
" 'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987',\n",
" 'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use `DataFrame.T` to transpose a dataframe.\n",
"\n",
"* Sometimes want to treat columns as rows and vice versa.\n",
"* Transpose (written `.T`) doesn’t copy the data, just changes the program’s view of it.\n",
"* Like `columns`, it is a member variable."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>country</th>\n",
" <th>Australia</th>\n",
" <th>New Zealand</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>gdpPercap_1952</th>\n",
" <td>10039.59564</td>\n",
" <td>10556.57566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1957</th>\n",
" <td>10949.64959</td>\n",
" <td>12247.39532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1962</th>\n",
" <td>12217.22686</td>\n",
" <td>13175.67800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1967</th>\n",
" <td>14526.12465</td>\n",
" <td>14463.91893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1972</th>\n",
" <td>16788.62948</td>\n",
" <td>16046.03728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1977</th>\n",
" <td>18334.19751</td>\n",
" <td>16233.71770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1982</th>\n",
" <td>19477.00928</td>\n",
" <td>17632.41040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1987</th>\n",
" <td>21888.88903</td>\n",
" <td>19007.19129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1992</th>\n",
" <td>23424.76683</td>\n",
" <td>18363.32494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_1997</th>\n",
" <td>26997.93657</td>\n",
" <td>21050.41377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_2002</th>\n",
" <td>30687.75473</td>\n",
" <td>23189.80135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdpPercap_2007</th>\n",
" <td>34435.36744</td>\n",
" <td>25185.00911</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"country Australia New Zealand\n",
"gdpPercap_1952 10039.59564 10556.57566\n",
"gdpPercap_1957 10949.64959 12247.39532\n",
"gdpPercap_1962 12217.22686 13175.67800\n",
"gdpPercap_1967 14526.12465 14463.91893\n",
"gdpPercap_1972 16788.62948 16046.03728\n",
"gdpPercap_1977 18334.19751 16233.71770\n",
"gdpPercap_1982 19477.00928 17632.41040\n",
"gdpPercap_1987 21888.88903 19007.19129\n",
"gdpPercap_1992 23424.76683 18363.32494\n",
"gdpPercap_1997 26997.93657 21050.41377\n",
"gdpPercap_2002 30687.75473 23189.80135\n",
"gdpPercap_2007 34435.36744 25185.00911"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.T"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use `DataFrame.describe` to get summary statistics about data.\n",
"\n",
"DataFrame.describe() gets the summary statistics of only the columns that have numerical data. All other columns are ignored, unless you use the argument `include='all'`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>gdpPercap_1952</th>\n",
" <th>gdpPercap_1957</th>\n",
" <th>gdpPercap_1962</th>\n",
" <th>gdpPercap_1967</th>\n",
" <th>gdpPercap_1972</th>\n",
" <th>gdpPercap_1977</th>\n",
" <th>gdpPercap_1982</th>\n",
" <th>gdpPercap_1987</th>\n",
" <th>gdpPercap_1992</th>\n",
" <th>gdpPercap_1997</th>\n",
" <th>gdpPercap_2002</th>\n",
" <th>gdpPercap_2007</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.00000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>10298.085650</td>\n",
" <td>11598.522455</td>\n",
" <td>12696.452430</td>\n",
" <td>14495.021790</td>\n",
" <td>16417.33338</td>\n",
" <td>17283.957605</td>\n",
" <td>18554.709840</td>\n",
" <td>20448.040160</td>\n",
" <td>20894.045885</td>\n",
" <td>24024.175170</td>\n",
" <td>26938.778040</td>\n",
" <td>29810.188275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>365.560078</td>\n",
" <td>917.644806</td>\n",
" <td>677.727301</td>\n",
" <td>43.986086</td>\n",
" <td>525.09198</td>\n",
" <td>1485.263517</td>\n",
" <td>1304.328377</td>\n",
" <td>2037.668013</td>\n",
" <td>3578.979883</td>\n",
" <td>4205.533703</td>\n",
" <td>5301.853680</td>\n",
" <td>6540.991104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>10039.595640</td>\n",
" <td>10949.649590</td>\n",
" <td>12217.226860</td>\n",
" <td>14463.918930</td>\n",
" <td>16046.03728</td>\n",
" <td>16233.717700</td>\n",
" <td>17632.410400</td>\n",
" <td>19007.191290</td>\n",
" <td>18363.324940</td>\n",
" <td>21050.413770</td>\n",
" <td>23189.801350</td>\n",
" <td>25185.009110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>10168.840645</td>\n",
" <td>11274.086022</td>\n",
" <td>12456.839645</td>\n",
" <td>14479.470360</td>\n",
" <td>16231.68533</td>\n",
" <td>16758.837652</td>\n",
" <td>18093.560120</td>\n",
" <td>19727.615725</td>\n",
" <td>19628.685413</td>\n",
" <td>22537.294470</td>\n",
" <td>25064.289695</td>\n",
" <td>27497.598692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10298.085650</td>\n",
" <td>11598.522455</td>\n",
" <td>12696.452430</td>\n",
" <td>14495.021790</td>\n",
" <td>16417.33338</td>\n",
" <td>17283.957605</td>\n",
" <td>18554.709840</td>\n",
" <td>20448.040160</td>\n",
" <td>20894.045885</td>\n",
" <td>24024.175170</td>\n",
" <td>26938.778040</td>\n",
" <td>29810.188275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>10427.330655</td>\n",
" <td>11922.958888</td>\n",
" <td>12936.065215</td>\n",
" <td>14510.573220</td>\n",
" <td>16602.98143</td>\n",
" <td>17809.077557</td>\n",
" <td>19015.859560</td>\n",
" <td>21168.464595</td>\n",
" <td>22159.406358</td>\n",
" <td>25511.055870</td>\n",
" <td>28813.266385</td>\n",
" <td>32122.777857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10556.575660</td>\n",
" <td>12247.395320</td>\n",
" <td>13175.678000</td>\n",
" <td>14526.124650</td>\n",
" <td>16788.62948</td>\n",
" <td>18334.197510</td>\n",
" <td>19477.009280</td>\n",
" <td>21888.889030</td>\n",
" <td>23424.766830</td>\n",
" <td>26997.936570</td>\n",
" <td>30687.754730</td>\n",
" <td>34435.367440</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 \\\n",
"count 2.000000 2.000000 2.000000 2.000000 \n",
"mean 10298.085650 11598.522455 12696.452430 14495.021790 \n",
"std 365.560078 917.644806 677.727301 43.986086 \n",
"min 10039.595640 10949.649590 12217.226860 14463.918930 \n",
"25% 10168.840645 11274.086022 12456.839645 14479.470360 \n",
"50% 10298.085650 11598.522455 12696.452430 14495.021790 \n",
"75% 10427.330655 11922.958888 12936.065215 14510.573220 \n",
"max 10556.575660 12247.395320 13175.678000 14526.124650 \n",
"\n",
" gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 \\\n",
"count 2.00000 2.000000 2.000000 2.000000 \n",
"mean 16417.33338 17283.957605 18554.709840 20448.040160 \n",
"std 525.09198 1485.263517 1304.328377 2037.668013 \n",
"min 16046.03728 16233.717700 17632.410400 19007.191290 \n",
"25% 16231.68533 16758.837652 18093.560120 19727.615725 \n",
"50% 16417.33338 17283.957605 18554.709840 20448.040160 \n",
"75% 16602.98143 17809.077557 19015.859560 21168.464595 \n",
"max 16788.62948 18334.197510 19477.009280 21888.889030 \n",
"\n",
" gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 \n",
"count 2.000000 2.000000 2.000000 2.000000 \n",
"mean 20894.045885 24024.175170 26938.778040 29810.188275 \n",
"std 3578.979883 4205.533703 5301.853680 6540.991104 \n",
"min 18363.324940 21050.413770 23189.801350 25185.009110 \n",
"25% 19628.685413 22537.294470 25064.289695 27497.598692 \n",
"50% 20894.045885 24024.175170 26938.778040 29810.188275 \n",
"75% 22159.406358 25511.055870 28813.266385 32122.777857 \n",
"max 23424.766830 26997.936570 30687.754730 34435.367440 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Writing manual data to a file\n",
"\n",
"There are several ways to structure your data when manually creating a Pandas `DataFrame`. Below shows how to create the `DataFrame` using a Python dictionary."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"manual_data = {\n",
" \"x\": [1, 2, 3],\n",
" \"y\": [2, 4, 6]\n",
"}\n",
"manual_df = pd.DataFrame(manual_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"The dictionary keys become the column names and the lists become the rows."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"cell_style": "center"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" <th>0</th>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" x y\n",
"0 1 2\n",
"1 2 4\n",
"2 3 6"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"manual_df"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Use the `.to_csv()` method to save the data to disk. If you don't want the row indices to be saved in the file, pass the argument `index=False` as shown below."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center"
},
"source": [
"```python\n",
"manual_df.to_csv(\"manual_data.csv\", index=False)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Selecting values\n",
"\n",
"To access a value at the position `[i, j]` of a DataFrame, we have two options, depending on what is the meaning of `i` in use. Remember that a DataFrame provides a index as a way to identify the rows of the table; a row, then, has a position inside the table as well as a label, which uniquely identifies its entry in the DataFrame.\n",
"\n",
"First, let's load the Europe subset of the Gapminder dataset sot that we have more rows and columns to work with."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\n",
" \"../../data/gapminder/gapminder_gdp_europe.csv\",\n",
" index_col=\"country\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use `DataFrame.iloc[..., ...]` to select values by their (entry) position\n",
"\n",
"Can specify location by numerical index analogously to 2D version of character selection in strings."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1601.056136"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.iloc[0, 0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"## Use `DataFrame.loc[..., ...]` to select values by their (entry) label.\n",
"\n",
"Can specify location by row name analogously to 2D version of dictionary keys."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1601.056136"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.loc[\"Albania\", \"gdpPercap_1952\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use `:` on its own to mean all columns or all rows.\n",
"\n",
"* Just like Python’s usual slicing notation."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gdpPercap_1952 1601.056136\n",
"gdpPercap_1957 1942.284244\n",
"gdpPercap_1962 2312.888958\n",
"gdpPercap_1967 2760.196931\n",
"gdpPercap_1972 3313.422188\n",
"gdpPercap_1977 3533.003910\n",
"gdpPercap_1982 3630.880722\n",
"gdpPercap_1987 3738.932735\n",
"gdpPercap_1992 2497.437901\n",
"gdpPercap_1997 3193.054604\n",
"gdpPercap_2002 4604.211737\n",
"gdpPercap_2007 5937.029526\n",
"Name: Albania, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Would get the same result printing data.loc[\"Albania\"] (without a second index).\n",
"data.loc[\"Albania\", :]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Select multiple columns or rows using `DataFrame.loc` and a named slice.\n",
"\n",
"You can slice several columns for a single row."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gdpPercap_1962 2312.888958\n",
"gdpPercap_1967 2760.196931\n",
"gdpPercap_1972 3313.422188\n",
"Name: Albania, dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.loc[\"Albania\", \"gdpPercap_1962\":\"gdpPercap_1972\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Method chaining\n",
"\n",
"Method chaining allows you to establish a visual pipeline of the transformations you apply to a dataset. Below, we use `.loc` to subset our dataset, then apply `.max()` to get the maximum values in the three remaining columns, and then take the average:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16303.415163333333"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data \\\n",
" .loc[\"Albania\":\"Poland\", \"gdpPercap_1962\":\"gdpPercap_1972\"] \\\n",
" .max() \\\n",
" .mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Use comparisons to select data based on value.\n",
"\n",
"* Comparison is applied element by element.\n",
"* Returns a similarly-shaped dataframe of `True` and `False`."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"data_subset = data \\\n",
" .loc[\"Albania\":\"Poland\", \"gdpPercap_1962\":\"gdpPercap_1972\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Select values or NaN using a Boolean mask.\n",
"\n",
"A frame full of Booleans is sometimes called a mask because of how it can be used.\n",
"\n",
"* Get the value where the mask is true, and NaN (Not a Number) where it is false.\n",
"* Useful because NaNs are ignored by operations like max, min, average, etc."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"cell_style": "center"
},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gdpPercap_1962</th>\n",
" <th>gdpPercap_1967</th>\n",
" <th>gdpPercap_1972</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Austria</th>\n",
" <td>10750.72111</td>\n",
" <td>12834.60240</td>\n",
" <td>16661.62560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belgium</th>\n",
" <td>10991.20676</td>\n",
" <td>13149.04119</td>\n",
" <td>16672.14356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bosnia and Herzegovina</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bulgaria</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Croatia</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Czech Republic</th>\n",
" <td>10136.86713</td>\n",
" <td>11399.44489</td>\n",
" <td>13108.45360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Denmark</th>\n",
" <td>13583.31351</td>\n",
" <td>15937.21123</td>\n",
" <td>18866.20721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Finland</th>\n",
" <td>NaN</td>\n",
" <td>10921.63626</td>\n",
" <td>14358.87590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>10560.48553</td>\n",
" <td>12999.91766</td>\n",
" <td>16107.19171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Germany</th>\n",
" <td>12902.46291</td>\n",
" <td>14745.62561</td>\n",
" <td>18016.18027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greece</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12724.82957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hungary</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10168.65611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceland</th>\n",
" <td>10350.15906</td>\n",
" <td>13319.89568</td>\n",
" <td>15798.06362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ireland</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Italy</th>\n",
" <td>NaN</td>\n",
" <td>10022.40131</td>\n",
" <td>12269.27378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Montenegro</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Netherlands</th>\n",
" <td>12790.84956</td>\n",
" <td>15363.25136</td>\n",
" <td>18794.74567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>13450.40151</td>\n",
" <td>16361.87647</td>\n",
" <td>18965.05551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poland</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gdpPercap_1962 gdpPercap_1967 gdpPercap_1972\n",
"country \n",
"Albania NaN NaN NaN\n",
"Austria 10750.72111 12834.60240 16661.62560\n",
"Belgium 10991.20676 13149.04119 16672.14356\n",
"Bosnia and Herzegovina NaN NaN NaN\n",
"Bulgaria NaN NaN NaN\n",
"Croatia NaN NaN NaN\n",
"Czech Republic 10136.86713 11399.44489 13108.45360\n",
"Denmark 13583.31351 15937.21123 18866.20721\n",
"Finland NaN 10921.63626 14358.87590\n",
"France 10560.48553 12999.91766 16107.19171\n",
"Germany 12902.46291 14745.62561 18016.18027\n",
"Greece NaN NaN 12724.82957\n",
"Hungary NaN NaN 10168.65611\n",
"Iceland 10350.15906 13319.89568 15798.06362\n",
"Ireland NaN NaN NaN\n",
"Italy NaN 10022.40131 12269.27378\n",
"Montenegro NaN NaN NaN\n",
"Netherlands 12790.84956 15363.25136 18794.74567\n",
"Norway 13450.40151 16361.87647 18965.05551\n",
"Poland NaN NaN NaN"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mask = data_subset > 10000\n",
"data_subset[mask]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The `.query()` method can also be used to find data using syntax that's reminiscent of R's filter rules and syntax in `dplyr`."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Austria</th>\n",
" <td>10750.72111</td>\n",
" <td>12834.60240</td>\n",
" <td>16661.62560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belgium</th>\n",
" <td>10991.20676</td>\n",
" <td>13149.04119</td>\n",
" <td>16672.14356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Denmark</th>\n",
" <td>13583.31351</td>\n",
" <td>15937.21123</td>\n",
" <td>18866.20721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>10560.48553</td>\n",
" <td>12999.91766</td>\n",
" <td>16107.19171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Germany</th>\n",
" <td>12902.46291</td>\n",
" <td>14745.62561</td>\n",
" <td>18016.18027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceland</th>\n",
" <td>10350.15906</td>\n",
" <td>13319.89568</td>\n",
" <td>15798.06362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Netherlands</th>\n",
" <td>12790.84956</td>\n",
" <td>15363.25136</td>\n",
" <td>18794.74567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>13450.40151</td>\n",
" <td>16361.87647</td>\n",
" <td>18965.05551</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gdpPercap_1962 gdpPercap_1967 gdpPercap_1972\n",
"country \n",
"Austria 10750.72111 12834.60240 16661.62560\n",
"Belgium 10991.20676 13149.04119 16672.14356\n",
"Denmark 13583.31351 15937.21123 18866.20721\n",
"France 10560.48553 12999.91766 16107.19171\n",
"Germany 12902.46291 14745.62561 18016.18027\n",
"Iceland 10350.15906 13319.89568 15798.06362\n",
"Netherlands 12790.84956 15363.25136 18794.74567\n",
"Norway 13450.40151 16361.87647 18965.05551"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_subset.query(\"gdpPercap_1972 > 10000 & gdpPercap_1967 > 12000\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Adding new columns with transformations\n",
"\n",
"You can create new columns that are the result of data transformations applied to existing columns. This example illustrates how to compute the ratio between the 1962 and 1972 GDP per capita for each country:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"cell_style": "center"
},
"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>gdpPercap_1962</th>\n",
" <th>gdpPercap_1967</th>\n",
" <th>gdpPercap_1972</th>\n",
" <th>ratio_62_to_72</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>2312.888958</td>\n",
" <td>2760.196931</td>\n",
" <td>3313.422188</td>\n",
" <td>0.698036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Austria</th>\n",
" <td>10750.721110</td>\n",
" <td>12834.602400</td>\n",
" <td>16661.625600</td>\n",
" <td>0.645238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belgium</th>\n",
" <td>10991.206760</td>\n",
" <td>13149.041190</td>\n",
" <td>16672.143560</td>\n",
" <td>0.659256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bosnia and Herzegovina</th>\n",
" <td>1709.683679</td>\n",
" <td>2172.352423</td>\n",
" <td>2860.169750</td>\n",
" <td>0.597756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bulgaria</th>\n",
" <td>4254.337839</td>\n",
" <td>5577.002800</td>\n",
" <td>6597.494398</td>\n",
" <td>0.644841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Croatia</th>\n",
" <td>5477.890018</td>\n",
" <td>6960.297861</td>\n",
" <td>9164.090127</td>\n",
" <td>0.597756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Czech Republic</th>\n",
" <td>10136.867130</td>\n",
" <td>11399.444890</td>\n",
" <td>13108.453600</td>\n",
" <td>0.773308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Denmark</th>\n",
" <td>13583.313510</td>\n",
" <td>15937.211230</td>\n",
" <td>18866.207210</td>\n",
" <td>0.719981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Finland</th>\n",
" <td>9371.842561</td>\n",
" <td>10921.636260</td>\n",
" <td>14358.875900</td>\n",
" <td>0.652686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>10560.485530</td>\n",
" <td>12999.917660</td>\n",
" <td>16107.191710</td>\n",
" <td>0.655638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Germany</th>\n",
" <td>12902.462910</td>\n",
" <td>14745.625610</td>\n",
" <td>18016.180270</td>\n",
" <td>0.716160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greece</th>\n",
" <td>6017.190733</td>\n",
" <td>8513.097016</td>\n",
" <td>12724.829570</td>\n",
" <td>0.472870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hungary</th>\n",
" <td>7550.359877</td>\n",
" <td>9326.644670</td>\n",
" <td>10168.656110</td>\n",
" <td>0.742513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceland</th>\n",
" <td>10350.159060</td>\n",
" <td>13319.895680</td>\n",
" <td>15798.063620</td>\n",
" <td>0.655154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ireland</th>\n",
" <td>6631.597314</td>\n",
" <td>7655.568963</td>\n",
" <td>9530.772896</td>\n",
" <td>0.695809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Italy</th>\n",
" <td>8243.582340</td>\n",
" <td>10022.401310</td>\n",
" <td>12269.273780</td>\n",
" <td>0.671888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Montenegro</th>\n",
" <td>4649.593785</td>\n",
" <td>5907.850937</td>\n",
" <td>7778.414017</td>\n",
" <td>0.597756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Netherlands</th>\n",
" <td>12790.849560</td>\n",
" <td>15363.251360</td>\n",
" <td>18794.745670</td>\n",
" <td>0.680555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>13450.401510</td>\n",
" <td>16361.876470</td>\n",
" <td>18965.055510</td>\n",
" <td>0.709220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poland</th>\n",
" <td>5338.752143</td>\n",
" <td>6557.152776</td>\n",
" <td>8006.506993</td>\n",
" <td>0.666802</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 \\\n",
"country \n",
"Albania 2312.888958 2760.196931 3313.422188 \n",
"Austria 10750.721110 12834.602400 16661.625600 \n",
"Belgium 10991.206760 13149.041190 16672.143560 \n",
"Bosnia and Herzegovina 1709.683679 2172.352423 2860.169750 \n",
"Bulgaria 4254.337839 5577.002800 6597.494398 \n",
"Croatia 5477.890018 6960.297861 9164.090127 \n",
"Czech Republic 10136.867130 11399.444890 13108.453600 \n",
"Denmark 13583.313510 15937.211230 18866.207210 \n",
"Finland 9371.842561 10921.636260 14358.875900 \n",
"France 10560.485530 12999.917660 16107.191710 \n",
"Germany 12902.462910 14745.625610 18016.180270 \n",
"Greece 6017.190733 8513.097016 12724.829570 \n",
"Hungary 7550.359877 9326.644670 10168.656110 \n",
"Iceland 10350.159060 13319.895680 15798.063620 \n",
"Ireland 6631.597314 7655.568963 9530.772896 \n",
"Italy 8243.582340 10022.401310 12269.273780 \n",
"Montenegro 4649.593785 5907.850937 7778.414017 \n",
"Netherlands 12790.849560 15363.251360 18794.745670 \n",
"Norway 13450.401510 16361.876470 18965.055510 \n",
"Poland 5338.752143 6557.152776 8006.506993 \n",
"\n",
" ratio_62_to_72 \n",
"country \n",
"Albania 0.698036 \n",
"Austria 0.645238 \n",
"Belgium 0.659256 \n",
"Bosnia and Herzegovina 0.597756 \n",
"Bulgaria 0.644841 \n",
"Croatia 0.597756 \n",
"Czech Republic 0.773308 \n",
"Denmark 0.719981 \n",
"Finland 0.652686 \n",
"France 0.655638 \n",
"Germany 0.716160 \n",
"Greece 0.472870 \n",
"Hungary 0.742513 \n",
"Iceland 0.655154 \n",
"Ireland 0.695809 \n",
"Italy 0.671888 \n",
"Montenegro 0.597756 \n",
"Netherlands 0.680555 \n",
"Norway 0.709220 \n",
"Poland 0.666802 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_subset[\"ratio_62_to_72\"] = \\\n",
" data_subset[\"gdpPercap_1962\"] / data_subset[\"gdpPercap_1972\"]\n",
"data_subset"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## `matplotlib` is the most widely used scientific plotting library in Python.\n",
"\n",
"* Commonly use a sub-library called matplotlib.pyplot.\n",
"* The Jupyter Notebook will render plots inline if we ask it to using a “magic” command.\n",
"\n",
"```python\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Simple plots are then (fairly) simple to create."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"time = [1, 2, 3]\n",
"position = [2, 4, 16]\n",
"\n",
"plt.plot(time, position)\n",
"plt.xlabel(\"Time (hr)\")\n",
"plt.ylabel(\"Position (m)\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## It's easy to create a scatter plot instead of line plot"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(time, position, \"o\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Plot data directly from a Pandas dataframe.\n",
"\n",
"* We can also plot Pandas dataframes.\n",
"* This implicitly uses `matplotlib.pyplot`.\n",
"* Before plotting, we convert the column headings from a `string` to `integer` data type, since they represent numerical values"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('../../data/gapminder/gapminder_gdp_oceania.csv', index_col='country')\n",
"years = data.columns.str.strip('gdpPercap_')\n",
"data.columns = years.astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Select and transform data, then plot it.\n",
"\n",
"By default, `DataFrame.plot` plots with the rows as the X axis.\n",
"We can transpose the data in order to plot multiple series."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data.T.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Different themes and style\n",
"\n",
"* The `kind=` input in the `DataFrame` is how you change the plot aesthetic.\n",
"* `plt.style.use()` lets you change the overall theme of your plots."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"cell_style": "split",
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data.T.plot(kind=\"bar\");"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"cell_style": "split",
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use(\"ggplot\")\n",
"data.T.plot(kind=\"bar\")\n",
"plt.ylabel(\"GDP per capita\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Data can also be plotted by calling the `matplotlib` `plot` function directly.\n",
"\n",
"* The command is `plt.plot(x, y)`\n",
"* The color / format of markers can also be specified as an optical argument: e.g. ‘b-‘ is a blue line, ‘g–’ is a green dashed line."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"years = data.columns\n",
"gdp_australia = data.loc['Australia']\n",
"\n",
"plt.plot(years, gdp_australia, 'g--');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Can plot many sets of data together.\n",
"\n",
"For complete control, extract the data from Pandas and use matplotlib directly."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gdp_australia = data.loc['Australia']\n",
"gdp_nz = data.loc['New Zealand']\n",
"\n",
"plt.plot(years, gdp_australia, 'b-', label='Australia')\n",
"plt.plot(years, gdp_nz, 'g-', label='New Zealand')\n",
"\n",
"plt.legend(loc='upper left')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('GDP per capita ($)');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Scatter plots with Pandas\n",
"\n",
"* Plot a scatter plot correlating the GDP of Australia and New Zealand\n",
"* Use either plt.scatter or DataFrame.plot.scatter"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.T.plot.scatter(x = \"Australia\", y = \"New Zealand\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Saving your plot to file\n",
"\n",
"If you are satisfied with the plot you see you may want to save it to a file, perhaps to include it in a publication. There is a function in the matplotlib.pyplot module that accomplishes this: savefig. Calling this function, e.g. with\n",
"\n",
"```python\n",
"plt.savefig('my_figure.png')\n",
"```\n",
"\n",
"will save the current figure to the file `my_figure.png`. The file format will automatically be deduced from the file name extension (other formats are pdf, ps, eps and svg)."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Note that functions in `plt` refer to a global figure variable and after a figure has been displayed to the screen (e.g. with `plt.show`) matplotlib will make this variable refer to a new empty figure. Therefore, make sure you call `plt.savefig` before the plot is displayed to the screen, otherwise you may find a file with an empty plot."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"When using dataframes, data is often generated and plotted to screen in one line, and `plt.savefig` seems not to be a possible approach. One possibility to save the figure to file is then to\n",
"\n",
"* save a reference to the current figure in a local variable (with `plt.gcf`)\n",
"* call the `savefig` class method from that varible."
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"```python\n",
"data.T.plot(kind='bar');\n",
"fig = plt.gcf() # get current figure\n",
"data.plot(kind='bar');\n",
"fig.savefig('gdp_comparison.png');\n",
"```"
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"source": [
"## Acknowledgments\n",
"\n",
"Content from the following episodes of the Software Carpentry lesson [Plotting and Programming in Python](https://swcarpentry.github.io/python-novice-gapminder/) made available under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/legalcode)\n",
"\n",
"* [Reading Tabular Data into DataFrames](https://swcarpentry.github.io/python-novice-gapminder/07-reading-tabular/index.html)\n",
"\n",
"* [Pandas DataFrames](https://swcarpentry.github.io/python-novice-gapminder/08-data-frames/index.html)\n",
"\n",
"* [Plotting](https://swcarpentry.github.io/python-novice-gapminder/09-plotting/index.html)"
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