Conditional Selection (Boolean Arrays)¶

In [1]:

import pandas as pd
import numpy as np

In [11]:

df = pd.DataFrame({
    'Population': [35.467, 63.951, 80.94 , 60.665, 127.061, 64.511, 318.523],
    'GDP': [
        1785387,
        2833687,
        3874437,
        2167744,
        4602367,
        2950039,
        17348075
    ],
    'Surface Area': [
        9984670,
        640679,
        357114,
        301336,
        377930,
        242495,
        9525067
    ],
    'HDI': [
        0.913,
        0.888,
        0.916,
        0.873,
        0.891,
        0.907,
        0.915
    ],
    'Continent': [
        'America',
        'Europe',
        'Europe',
        'Europe',
        'Asia',
        'Europe',
        'America'
    ]
}, columns=['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'])

df.index = [
    'Canada',
    'France',
    'Germany',
    'Italy',
    'Japan',
    'United Kingdom',
    'United States',
]

In [12]:

df

Out[12]:

	Population	GDP	Surface Area	HDI	Continent
Canada	35.467	1785387	9984670	0.913	America
France	63.951	2833687	640679	0.888	Europe
Germany	80.940	3874437	357114	0.916	Europe
Italy	60.665	2167744	301336	0.873	Europe
Japan	127.061	4602367	377930	0.891	Asia
United Kingdom	64.511	2950039	242495	0.907	Europe
United States	318.523	17348075	9525067	0.915	America

In [13]:

df['Population'] > 70

Out[13]:

Canada            False
France            False
Germany            True
Italy             False
Japan              True
United Kingdom    False
United States      True
Name: Population, dtype: bool

In [14]:

df.loc[df['Population'] > 70]

Out[14]:

	Population	GDP	Surface Area	HDI	Continent
Germany	80.940	3874437	357114	0.916	Europe
Japan	127.061	4602367	377930	0.891	Asia
United States	318.523	17348075	9525067	0.915	America

In [15]:

df.loc[df['Population'] > 70, 'Population']

Out[15]:

Germany           80.940
Japan            127.061
United States    318.523
Name: Population, dtype: float64

In [16]:

df.loc[df['Population'] > 70, ['Population', 'GDP']]

Out[16]:

	Population	GDP
Germany	80.940	3874437
Japan	127.061	4602367
United States	318.523	17348075

Dropping Stuff¶

In [17]:

df.drop('Canada')

Out[17]:

	Population	GDP	Surface Area	HDI	Continent
France	63.951	2833687	640679	0.888	Europe
Germany	80.940	3874437	357114	0.916	Europe
Italy	60.665	2167744	301336	0.873	Europe
Japan	127.061	4602367	377930	0.891	Asia
United Kingdom	64.511	2950039	242495	0.907	Europe
United States	318.523	17348075	9525067	0.915	America

In [18]:

df.drop(columns=['Surface Area', 'HDI'])

Out[18]:

	Population	GDP	Continent
Canada	35.467	1785387	America
France	63.951	2833687	Europe
Germany	80.940	3874437	Europe
Italy	60.665	2167744	Europe
Japan	127.061	4602367	Asia
United Kingdom	64.511	2950039	Europe
United States	318.523	17348075	America

Operations¶

In [20]:

df[['Population']] / 10

Out[20]:

	Population
Canada	3.5467
France	6.3951
Germany	8.0940
Italy	6.0665
Japan	12.7061
United Kingdom	6.4511
United States	31.8523

Modifying DataFrames¶

In [21]:

langs = pd.Series(
    ['French', 'German', 'Italian'],
    index=['France', 'Germany', 'Italy'],
    name='Language'
)

In [22]:

df['Language'] = langs

In [23]:

df

Out[23]:

	Population	GDP	Surface Area	HDI	Continent	Language
Canada	35.467	1785387	9984670	0.913	America	NaN
France	63.951	2833687	640679	0.888	Europe	French
Germany	80.940	3874437	357114	0.916	Europe	German
Italy	60.665	2167744	301336	0.873	Europe	Italian
Japan	127.061	4602367	377930	0.891	Asia	NaN
United Kingdom	64.511	2950039	242495	0.907	Europe	NaN
United States	318.523	17348075	9525067	0.915	America	NaN

Replacing values per column¶

In [25]:

df['Languages'] = 'English'

In [26]:

df

Out[26]:

	Population	GDP	Surface Area	HDI	Continent	Language	Languages
Canada	35.467	1785387	9984670	0.913	America	NaN	English
France	63.951	2833687	640679	0.888	Europe	French	English
Germany	80.940	3874437	357114	0.916	Europe	German	English
Italy	60.665	2167744	301336	0.873	Europe	Italian	English
Japan	127.061	4602367	377930	0.891	Asia	NaN	English
United Kingdom	64.511	2950039	242495	0.907	Europe	NaN	English
United States	318.523	17348075	9525067	0.915	America	NaN	English

Renaming columns¶

In [27]:

df.rename(
    columns={
        'HDI': 'Human Development Index',
        'Anual Popcorn Consumption': 'APC'
    }, index={
        'United States': 'USA',
        'United Kingdom': 'UK',
        'Argentina': 'AR'
    })

Out[27]:

	Population	GDP	Surface Area	Human Development Index	Continent	Language	Languages
Canada	35.467	1785387	9984670	0.913	America	NaN	English
France	63.951	2833687	640679	0.888	Europe	French	English
Germany	80.940	3874437	357114	0.916	Europe	German	English
Italy	60.665	2167744	301336	0.873	Europe	Italian	English
Japan	127.061	4602367	377930	0.891	Asia	NaN	English
UK	64.511	2950039	242495	0.907	Europe	NaN	English
USA	318.523	17348075	9525067	0.915	America	NaN	English

In [28]:

df.rename(index=str.upper)

Out[28]:

	Population	GDP	Surface Area	HDI	Continent	Language	Languages
CANADA	35.467	1785387	9984670	0.913	America	NaN	English
FRANCE	63.951	2833687	640679	0.888	Europe	French	English
GERMANY	80.940	3874437	357114	0.916	Europe	German	English
ITALY	60.665	2167744	301336	0.873	Europe	Italian	English
JAPAN	127.061	4602367	377930	0.891	Asia	NaN	English
UNITED KINGDOM	64.511	2950039	242495	0.907	Europe	NaN	English
UNITED STATES	318.523	17348075	9525067	0.915	America	NaN	English

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