Pandas dataframes

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.

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

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"])
Albania                    1601.056136
Austria                    6137.076492
Belgium                    8343.105127
Bosnia and Herzegovina      973.533195
Bulgaria                   2444.286648
Croatia                    3119.236520
Czech Republic             6876.140250
Denmark                    9692.385245
Finland                    6424.519071
France                     7029.809327
Germany                    7144.114393
Greece                     3530.690067
Hungary                    5263.673816
Iceland                    7267.688428
Ireland                    5210.280328
Italy                      4931.404155
Montenegro                 2647.585601
Netherlands                8941.571858
Norway                    10095.421720
Poland                     4029.329699
Portugal                   3068.319867
Romania                    3144.613186
Serbia                     3581.459448
Slovak Republic            5074.659104
Slovenia                   4215.041741
Spain                      3834.034742
Sweden                     8527.844662
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.

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.

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.

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
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
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
             gdpPercap_1962  gdpPercap_1967  gdpPercap_1972
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

Group By: split-apply-combine

Pandas vectorizing methods and grouping operations are features that provide users much flexibility to analyse their data.

For instance, let’s say we want to have a clearer view on how the European countries split themselves according to their GDP.

  1. We may have a glance by splitting the countries in two groups during the years surveyed, those who presented a GDP higher than the European average and those with a lower GDP.

  2. We then estimate a wealthy score based on the historical (from 1962 to 2007) values, where we account how many times a country has participated in the groups of lower or higher GDP

mask_higher = data > data.mean()
wealth_score = mask_higher.aggregate('sum', axis=1) / len(data.columns)
Albania                   0.000000
Austria                   1.000000
Belgium                   1.000000
Bosnia and Herzegovina    0.000000
Bulgaria                  0.000000
Croatia                   0.000000
Czech Republic            0.500000
Denmark                   1.000000
Finland                   1.000000
France                    1.000000
Germany                   1.000000
Greece                    0.333333
Hungary                   0.000000
Iceland                   1.000000
Ireland                   0.333333
Italy                     0.500000
Montenegro                0.000000
Netherlands               1.000000
Norway                    1.000000
Poland                    0.000000
Portugal                  0.000000
Romania                   0.000000
Serbia                    0.000000
Slovak Republic           0.000000
Slovenia                  0.333333
Spain                     0.333333
Sweden                    1.000000
Switzerland               1.000000
Turkey                    0.000000
United Kingdom            1.000000
dtype: float64

Finally, for each group in the wealth_score table, we sum their (financial) contribution across the years surveyed using chained methods:

gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007
0.000000 36916.854200 46110.918793 56850.065437 71324.848786 88569.346898 104459.358438 113553.768507 119649.599409 92380.047256 103772.937598 118590.929863 149577.357928
0.333333 16790.046878 20942.456800 25744.935321 33567.667670 45277.839976 53860.456750 59679.634020 64436.912960 67918.093220 80876.051580 102086.795210 122803.729520
0.500000 11807.544405 14505.000150 18380.449470 21421.846200 25377.727380 29056.145370 31914.712050 35517.678220 36310.666080 40723.538700 45564.308390 51403.028210
1.000000 104317.277560 127332.008735 149989.154201 178000.350040 215162.343140 241143.412730 263388.781960 296825.131210 315238.235970 346930.926170 385109.939210 427850.333420

Exercise: 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.

Exercise: 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'])

Exercise: Reconstructing Data

Explain what each line in the following short program does: what is in first, second, etc.?

first = pd.read_csv('data/gapminder_all.csv', index_col='country')
second = first[first['continent'] == 'Americas']
third = second.drop('Puerto Rico')
fourth = third.drop('continent', axis = 1)

Exercise: Selecting Indices

Explain in simple terms what idxmin and idxmax do in the short program below. When would you use these methods?

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

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 all countries in 1982.

  2. GDP per capita for Denmark for all years.

  3. GDP per capita for all countries for years after 1985.

  4. GDP per capita for each country in 2007 as a multiple of GDP per capita for that country in 1952.








Pandas is smart enough to recognize the number at the end of the column label and does not give you an error, although no column named gdpPercap_1985 actually exists. This is useful if new columns are added to the CSV file later.



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 

{: .language-python} This command returns:


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.


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



Poland’s borders have been stable since 1945, but changed several times in the years before then. How would you handle this if you were creating a table of GDP per capita for Poland for the entire twentieth century?

Test a directive


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