Pandas DataFrames

Overview

Teaching: 12 min
Exercises: 13 min

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.

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.

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

1601.056136

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

• Can specify location by row name analogously to 2D version of dictionary keys.

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

1601.056136

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

• Just like Python’s usual slicing notation.

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

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).

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

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"]

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

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

             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.

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

gdpPercap_1962    13450.40151
gdpPercap_1967    16361.87647
gdpPercap_1972    18965.05551
dtype: float64

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

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.

# 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)

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.

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

             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.

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

       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

Selection of Individual Values

Assume Pandas has been imported into your notebook and the Gapminder GDP data for Europe has been loaded:

import pandas as pd

df = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')

Write an expression to find the Per Capita GDP of Serbia in 2007.

Solution

The selection can be done by using the labels for both the row (“Serbia”) and the column (“gdpPercap_2007”):

print(df.loc['Serbia', 'gdpPercap_2007'])

The output is

9786.534714

Extent of Slicing

1. Do the two statements below produce the same output?
2. Based on this, what rule governs what is included (or not) in numerical slices and named slices in Pandas?

print(df.iloc[0:2, 0:2])
print(df.loc['Albania':'Belgium', 'gdpPercap_1952':'gdpPercap_1962'])

Solution

No, they do not produce the same output! The output of the first statement is:

        gdpPercap_1952  gdpPercap_1957
country                                
Albania     1601.056136     1942.284244
Austria     6137.076492     8842.598030

The second statement gives:

        gdpPercap_1952  gdpPercap_1957  gdpPercap_1962
country                                                
Albania     1601.056136     1942.284244     2312.888958
Austria     6137.076492     8842.598030    10750.721110
Belgium     8343.105127     9714.960623    10991.206760

Clearly, the second statement produces an additional column and an additional row compared to the first statement.
What conclusion can we draw? We see that a numerical slice, 0:2, omits the final index (i.e. index 2) in the range provided, while a named slice, ‘gdpPercap_1952’:’gdpPercap_1962’, includes the final element.

Practice with Selection

Assume Pandas has been imported and the Gapminder GDP data for Europe has been loaded. Write an expression to select each of the following:

1. GDP per capita for each country in 1982.
2. GDP per capita for Denmark for each year.
3. GDP per capita for each country in 2007 as a ratio to the GDP per capita for that country in 1952.

Solution

1:

data['gdpPercap_1982']

2:

data.loc['Denmark',:]

3:

data['gdpPercap_2007']/data['gdpPercap_1952']

Exploring available methods using the dir() function

Python includes a dir() function that can be used to display all of the available methods (functions) that are built into a data object. In Episode 4, we used some methods with a string. But we can see many more are available by using dir():

my_string = 'Hello world!'   # creation of a string object 
dir(my_string)

This command returns:

['__add__',
...
'__subclasshook__',
'capitalize',
'casefold',
'center',
...
'upper',
'zfill']

You can use help() or Shift+Tab to get more information about what these methods do.

Assume Pandas has been imported and the Gapminder GDP data for Europe has been loaded as data. Then, use dir() to find the function that prints out the median per-capita GDP across all European countries for each year that information is available.

Solution

Among many choices, dir() lists the median() function as a possibility. Thus,

data.median()

Key Points

• Use DataFrame.iloc[..., ...] to select values by integer location.

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

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

• Result of slicing can be used in further operations.

• Use comparisons to select data based on value.

• Select values or NaN using a Boolean mask.