Pandas DataFrames

Last updated on 2024-05-02 | Edit this page

Overview

Questions

How can I do statistical analysis of tabular data?

Objectives

Select individual values from a Pandas dataframe.
Select entire rows or entire columns from a dataframe.
Select a subset of both rows and columns from a dataframe in a single operation.
Select a subset of a dataframe by a single Boolean criterion.

Note about Pandas DataFrames/Series

A DataFrame is a collection of Series; The DataFrame is the way Pandas represents a table, and Series is the data-structure Pandas use to represent a column.

Pandas is built on top of the Numpy library, which in practice means that most of the methods defined for Numpy Arrays apply to Pandas Series/DataFrames.

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.

Selecting values

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 an 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.

Use `DataFrame.iloc[..., ...]` to select values by their (entry) position

Can specify location by numerical index analogously to 2D version of character selection in strings.

PYTHON

import pandas as pd
data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
print(data.iloc[0, 0])

OUTPUT

1601.056136

Use `DataFrame.loc[..., ...]` to select values by their (entry) label.

Can specify location by row and/or column name.

PYTHON

print(data.loc["Albania", "gdpPercap_1952"])

OUTPUT

1601.056136

Use `:` on its own to mean all columns or all rows.

Just like Python’s usual slicing notation.

PYTHON

print(data.loc["Albania", :])

OUTPUT

gdpPercap_1952    1601.056136
gdpPercap_1957    1942.284244
gdpPercap_1962    2312.888958
gdpPercap_1967    2760.196931
gdpPercap_1972    3313.422188
gdpPercap_1977    3533.003910
gdpPercap_1982    3630.880722
gdpPercap_1987    3738.932735
gdpPercap_1992    2497.437901
gdpPercap_1997    3193.054604
gdpPercap_2002    4604.211737
gdpPercap_2007    5937.029526
Name: Albania, dtype: float64

Would get the same result printing data.loc["Albania"] (without a second index).

PYTHON

print(data.loc[:, "gdpPercap_1952"])

OUTPUT

country
Albania                    1601.056136
Austria                    6137.076492
Belgium                    8343.105127
⋮ ⋮ ⋮
Switzerland               14734.232750
Turkey                     1969.100980
United Kingdom             9979.508487
Name: gdpPercap_1952, dtype: float64

Would get the same result printing data["gdpPercap_1952"]
Also get the same result printing data.gdpPercap_1952 (not recommended, because easily confused with . notation for methods)

Select multiple columns or rows using `DataFrame.loc` and a named slice.

PYTHON

print(data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972'])

OUTPUT

             gdpPercap_1962  gdpPercap_1967  gdpPercap_1972
country
Italy           8243.582340    10022.401310    12269.273780
Montenegro      4649.593785     5907.850937     7778.414017
Netherlands    12790.849560    15363.251360    18794.745670
Norway         13450.401510    16361.876470    18965.055510
Poland          5338.752143     6557.152776     8006.506993

In the above code, we discover that slicing using loc is inclusive at both ends, which differs from slicing using iloc, where slicing indicates everything up to but not including the final index.

Result of slicing can be used in further operations.

Usually don’t just print a slice.
All the statistical operators that work on entire dataframes work the same way on slices.
E.g., calculate max of a slice.

PYTHON

print(data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972'].max())

OUTPUT

gdpPercap_1962    13450.40151
gdpPercap_1967    16361.87647
gdpPercap_1972    18965.05551
dtype: float64

PYTHON

print(data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972'].min())

OUTPUT

gdpPercap_1962    4649.593785
gdpPercap_1967    5907.850937
gdpPercap_1972    7778.414017
dtype: float64

Use comparisons to select data based on value.

Comparison is applied element by element.
Returns a similarly-shaped dataframe of True and False.

PYTHON

# Use a subset of data to keep output readable.
subset = data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972']
print('Subset of data:\n', subset)

# Which values were greater than 10000 ?
print('\nWhere are values large?\n', subset > 10000)

OUTPUT

Subset of data:
             gdpPercap_1962  gdpPercap_1967  gdpPercap_1972
country
Italy           8243.582340    10022.401310    12269.273780
Montenegro      4649.593785     5907.850937     7778.414017
Netherlands    12790.849560    15363.251360    18794.745670
Norway         13450.401510    16361.876470    18965.055510
Poland          5338.752143     6557.152776     8006.506993

Where are values large?
            gdpPercap_1962 gdpPercap_1967 gdpPercap_1972
country
Italy                False           True           True
Montenegro           False          False          False
Netherlands           True           True           True
Norway                True           True           True
Poland               False          False          False

Select values or NaN using a Boolean mask.

A frame full of Booleans is sometimes called a mask because of how it can be used.

PYTHON

mask = subset > 10000
print(subset[mask])

OUTPUT

             gdpPercap_1962  gdpPercap_1967  gdpPercap_1972
country
Italy                   NaN     10022.40131     12269.27378
Montenegro              NaN             NaN             NaN
Netherlands     12790.84956     15363.25136     18794.74567
Norway          13450.40151     16361.87647     18965.05551
Poland                  NaN             NaN             NaN

Get the value where the mask is true, and NaN (Not a Number) where it is false.
Useful because NaNs are ignored by operations like max, min, average, etc.

PYTHON

print(subset[subset > 10000].describe())

OUTPUT

       gdpPercap_1962  gdpPercap_1967  gdpPercap_1972
count        2.000000        3.000000        3.000000
mean     13120.625535    13915.843047    16676.358320
std        466.373656     3408.589070     3817.597015
min      12790.849560    10022.401310    12269.273780
25%      12955.737547    12692.826335    15532.009725
50%      13120.625535    15363.251360    18794.745670
75%      13285.513523    15862.563915    18879.900590
max      13450.401510    16361.876470    18965.055510

Pandas DataFrames

Overview

Questions

Objectives

Note about Pandas DataFrames/Series

Selecting values

Use DataFrame.iloc[..., ...] to select values by their (entry) position

PYTHON

OUTPUT

Use DataFrame.loc[..., ...] to select values by their (entry) label.

PYTHON

OUTPUT

Use : on its own to mean all columns or all rows.

PYTHON

OUTPUT

PYTHON

OUTPUT

Select multiple columns or rows using DataFrame.loc and a named slice.

PYTHON

OUTPUT

Result of slicing can be used in further operations.

PYTHON

OUTPUT

PYTHON

OUTPUT

Use comparisons to select data based on value.

PYTHON

OUTPUT

Select values or NaN using a Boolean mask.

PYTHON

OUTPUT

PYTHON

OUTPUT

Group By: split-apply-combine

Use `DataFrame.iloc[..., ...]` to select values by their (entry) position

Use `DataFrame.loc[..., ...]` to select values by their (entry) label.

Use `:` on its own to mean all columns or all rows.

Select multiple columns or rows using `DataFrame.loc` and a named slice.