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How to: Pandas Tutorial – Indexing, selecting and assigning in Python

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How to: Pandas Tutorial – Indexing, selecting and assigning Pandas Tutorial – Indexing, selecting and assigning: Pro data scientists do this dozens of times a day. You can, too!

Pandas Tutorial – Indexing, selecting and assigning in Python: Pro data scientists do this dozens of times a day. You can, too!

Introduction

Selecting specific values of a pandas DataFrame or Series to work on is an implicit step in almost any data operation you’ll run, so one of the first things you need to learn in working with data in Python is how to go about selecting the data points relevant to you quickly and effectively.

For this exercise we will continue using the famous wine review dataframe. You can obtain this from Kaggle, following this link.

import pandas as pd reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0) pd.set_option('max_rows', 5)

Download the most comprehensive Python Pandas Cheat Sheet here

Python-Pandas-Cheat-Sheets-1-8-v1.0.2.pdf

Native accessors

Native Python objects provide good ways of indexing data. Pandas carries all of these over, which helps make it easy to start with.

Consider this DataFrame:

reviews
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
0ItalyAromas include tropical fruit, broom, brimston…Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
1PortugalThis is ripe and fruity, a wine that is smooth…Avidagos8715.0DouroNaNNaNRoger Voss@vossrogerQuinta dos Avidagos 2011 Avidagos Red (Douro)Portuguese RedQuinta dos Avidagos
129969FranceA dry style of Pinot Gris, this is crisp with …NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970FranceBig, rich and off-dry, this is powered by inte…Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Car…GewürztraminerDomaine Schoffit

129971 rows × 13 columns

In Python, we can access the property of an object by accessing it as an attribute. A book object, for example, might have a title property, which we can access by calling book.title. Columns in a pandas DataFrame work in much the same way.

Hence to access the country property of reviews we can use:

reviews.country
0 Italy 1 Portugal ... 129969 France 129970 France Name: country, Length: 129971, dtype: object

If we have a Python dictionary, we can access its values using the indexing ([]) operator. We can do the same with columns in a DataFrame:

reviews['country']
0 Italy 1 Portugal ... 129969 France 129970 France Name: country, Length: 129971, dtype: object

These are the two ways of selecting a specific Series out of a DataFrame. Neither of them is more or less syntactically valid than the other, but the indexing operator [] does have the advantage that it can handle column names with reserved characters in them (e.g. if we had a country providence column, reviews.country providence wouldn’t work).

Doesn’t a pandas Series look kind of like a fancy dictionary? It pretty much is, so it’s no surprise that, to drill down to a single specific value, we need only use the indexing operator [] once more:

reviews.iloc[0]
'Italy'

Indexing in pandas

The indexing operator and attribute selection are nice because they work just like they do in the rest of the Python ecosystem. As a novice, this makes them easy to pick up and use. However, pandas has its own accessor operators, loc and iloc. For more advanced operations, these are the ones you’re supposed to be using.

Index-based selection

Pandas indexing works in one of two paradigms. The first is index-based selection: selecting data based on its numerical position in the data. iloc follows this paradigm.

To select the first row of data in a DataFrame, we may use the following:

reviews.iloc[0]
country Italy description Aromas include tropical fruit, broom, brimston... ... variety White Blend winery Nicosia Name: 0, Length: 13, dtype: object

Both loc and iloc are row-first, column-second. This is the opposite of what we do in native Python, which is column-first, row-second.

This means that it’s marginally easier to retrieve rows, and marginally harder to get retrieve columns. To get a column with iloc, we can do the following:

reviews.iloc[:, 0]
0 Italy 1 Portugal ... 129969 France 129970 France Name: country, Length: 129971, dtype: object

On its own, the : operator, which also comes from native Python, means “everything”. When combined with other selectors, however, it can be used to indicate a range of values. For example, to select the country column from just the first, second, and third row, we would do:

reviews.iloc[:3, 0]
0 Italy 1 Portugal 2 US Name: country, dtype: object

Or, to select just the second and third entries, we would do:

reviews.iloc[1:3, 0]
1 Portugal 2 US Name: country, dtype: object

It’s also possible to pass a list:

reviews.iloc[[0, 1, 2], 0]
0 Italy 1 Portugal 2 US Name: country, dtype: object

Finally, it’s worth knowing that negative numbers can be used in selection. This will start counting forwards from the end of the values. So for example here are the last five elements of the dataset.

reviews.iloc[-5:]
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
129966GermanyNotes of honeysuckle and cantaloupe sweeten th…Brauneberger Juffer-Sonnenuhr Spätlese9028.0MoselNaNNaNAnna Lee C. IijimaNaNDr. H. Thanisch (Erben Müller-Burggraef) 2013 …RieslingDr. H. Thanisch (Erben Müller-Burggraef)
129967USCitation is given as much as a decade of bottl…NaN9075.0OregonOregonOregon OtherPaul Gregutt@paulgwineCitation 2004 Pinot Noir (Oregon)Pinot NoirCitation
129968FranceWell-drained gravel soil gives this wine its c…Kritt9030.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Gresser 2013 Kritt Gewurztraminer (Als…GewürztraminerDomaine Gresser
129969FranceA dry style of Pinot Gris, this is crisp with …NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970FranceBig, rich and off-dry, this is powered by inte…Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Car…GewürztraminerDomaine Schoffit

Label-based selection

The second paradigm for attribute selection is the one followed by the loc operator: label-based selection. In this paradigm, it’s the data index value, not its position, which matters.

For example, to get the first entry in reviews, we would now do the following:

reviews.loc[0, 'country']
'Italy'

iloc is conceptually simpler than loc because it ignores the dataset’s indices. When we use iloc we treat the dataset like a big matrix (a list of lists), one that we have to index into by position. loc, by contrast, uses the information in the indices to do its work. Since your dataset usually has meaningful indices, it’s usually easier to do things using loc instead. For example, here’s one operation that’s much easier using loc:

reviews.loc[:, ['taster_name', 'taster_twitter_handle', 'points']]
taster_nametaster_twitter_handlepoints
0Kerin O’Keefe@kerinokeefe87
1Roger Voss@vossroger87
129969Roger Voss@vossroger90
129970Roger Voss@vossroger90

129971 rows × 3 columns

Choosing between loc and iloc

When choosing or transitioning between loc and iloc, there is one “gotcha” worth keeping in mind, which is that the two methods use slightly different indexing schemes.

iloc uses the Python stdlib indexing scheme, where the first element of the range is included and the last one excluded. So 0:10 will select entries 0,...,9. loc, meanwhile, indexes inclusively. So 0:10 will select entries 0,...,10.

Why the change? Remember that loc can index any stdlib type: strings, for example. If we have a DataFrame with index values Apples, ..., Potatoes, ..., and we want to select “all the alphabetical fruit choices between Apples and Potatoes”, then it’s a lot more convenient to index df.loc['Apples':'Potatoes'] than it is to index something like df.loc['Apples', 'Potatoet'] (t coming after s in the alphabet).

This is particularly confusing when the DataFrame index is a simple numerical list, e.g. 0,...,1000. In this case df.iloc[0:1000] will return 1000 entries, while df.loc[0:1000] return 1001 of them! To get 1000 elements using loc, you will need to go one lower and ask for df.loc[0:999].

Otherwise, the semantics of using loc are the same as those for iloc.

Manipulating the index

Label-based selection derives its power from the labels in the index. Critically, the index we use is not immutable. We can manipulate the index in any way we see fit.

The set_index() method can be used to do the job. Here is what happens when we set_index to the title field:

reviews.set_index("title")
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handlevarietywinery
title
Nicosia 2013 Vulkà Bianco (Etna)ItalyAromas include tropical fruit, broom, brimston…Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeWhite BlendNicosia
Quinta dos Avidagos 2011 Avidagos Red (Douro)PortugalThis is ripe and fruity, a wine that is smooth…Avidagos8715.0DouroNaNNaNRoger Voss@vossrogerPortuguese RedQuinta dos Avidagos
Domaine Marcel Deiss 2012 Pinot Gris (Alsace)FranceA dry style of Pinot Gris, this is crisp with …NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerPinot GrisDomaine Marcel Deiss
Domaine Schoffit 2012 Lieu-dit Harth Cuvée Caroline Gewurztraminer (Alsace)FranceBig, rich and off-dry, this is powered by inte…Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerGewürztraminerDomaine Schoffit

129971 rows × 12 columns

This is useful if you can come up with an index for the dataset which is better than the current one.

Conditional selection

So far we’ve been indexing various strides of data, using structural properties of the DataFrame itself. To do interesting things with the data, however, we often need to ask questions based on conditions.

For example, suppose that we’re interested specifically in better-than-average wines produced in Italy.

We can start by checking if each wine is Italian or not:

reviews.country == 'Italy'
0 True 1 False ... 129969 False 129970 False Name: country, Length: 129971, dtype: bool

This operation produced a Series of True/False booleans based on the country of each record. This result can then be used inside of loc to select the relevant data:

reviews.loc[reviews.country == 'Italy']
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
0ItalyAromas include tropical fruit, broom, brimston…Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
6ItalyHere’s a bright, informal red that opens with …Belsito8716.0Sicily & SardiniaVittoriaNaNKerin O’Keefe@kerinokeefeTerre di Giurfo 2013 Belsito Frappato (Vittoria)FrappatoTerre di Giurfo
129961ItalyIntense aromas of wild cherry, baking spice, t…NaN9030.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCOS 2013 Frappato (Sicilia)FrappatoCOS
129962ItalyBlackberry, cassis, grilled herb and toasted a…Sàgana Tenuta San Giacomo9040.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCusumano 2012 Sàgana Tenuta San Giacomo Nero d…Nero d’AvolaCusumano

19540 rows × 13 columns

This DataFrame has ~20,000 rows. The original had ~130,000. That means that around 15% of wines originate from Italy.

We also wanted to know which ones are better than average. Wines are reviewed on a 80-to-100 point scale, so this could mean wines that accrued at least 90 points.

We can use the ampersand (&) to bring the two questions together:

reviews.loc[(reviews.country == 'Italy') & (reviews.points >= 90)]
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
120ItalySlightly backward, particularly given the vint…Bricco Rocche Prapó9270.0PiedmontBaroloNaNNaNNaNCeretto 2003 Bricco Rocche Prapó (Barolo)NebbioloCeretto
130ItalyAt the first it was quite muted and subdued, b…Bricco Rocche Brunate9170.0PiedmontBaroloNaNNaNNaNCeretto 2003 Bricco Rocche Brunate (Barolo)NebbioloCeretto
129961ItalyIntense aromas of wild cherry, baking spice, t…NaN9030.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCOS 2013 Frappato (Sicilia)FrappatoCOS
129962ItalyBlackberry, cassis, grilled herb and toasted a…Sàgana Tenuta San Giacomo9040.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCusumano 2012 Sàgana Tenuta San Giacomo Nero d…Nero d’AvolaCusumano

6648 rows × 13 columns

Suppose we’ll buy any wine that’s made in Italy or which is rated above average. For this we use a pipe (|):

reviews.loc[(reviews.country == 'Italy') | (reviews.points >= 90)]
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
0ItalyAromas include tropical fruit, broom, brimston…Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
6ItalyHere’s a bright, informal red that opens with …Belsito8716.0Sicily & SardiniaVittoriaNaNKerin O’Keefe@kerinokeefeTerre di Giurfo 2013 Belsito Frappato (Vittoria)FrappatoTerre di Giurfo
129969FranceA dry style of Pinot Gris, this is crisp with …NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970FranceBig, rich and off-dry, this is powered by inte…Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Car…GewürztraminerDomaine Schoffit

61937 rows × 13 columns

Pandas comes with a few built-in conditional selectors, two of which we will highlight here.

The first is isin. isin is lets you select data whose value “is in” a list of values. For example, here’s how we can use it to select wines only from Italy or France:

reviews.loc[reviews.country.isin(['Italy', 'France'])]
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
0ItalyAromas include tropical fruit, broom, brimston…Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
6ItalyHere’s a bright, informal red that opens with …Belsito8716.0Sicily & SardiniaVittoriaNaNKerin O’Keefe@kerinokeefeTerre di Giurfo 2013 Belsito Frappato (Vittoria)FrappatoTerre di Giurfo
129969FranceA dry style of Pinot Gris, this is crisp with …NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970FranceBig, rich and off-dry, this is powered by inte…Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Car…GewürztraminerDomaine Schoffit

41633 rows × 13 columns

The second is isnull (and its companion notnull). These methods let you highlight values which are (or are not) empty (NaN). For example, to filter out wines lacking a price tag in the dataset, here’s what we would do:

reviews.loc[reviews.price.notnull()]
countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
1PortugalThis is ripe and fruity, a wine that is smooth…Avidagos8715.0DouroNaNNaNRoger Voss@vossrogerQuinta dos Avidagos 2011 Avidagos Red (Douro)Portuguese RedQuinta dos Avidagos
2USTart and snappy, the flavors of lime flesh and…NaN8714.0OregonWillamette ValleyWillamette ValleyPaul Gregutt@paulgwineRainstorm 2013 Pinot Gris (Willamette Valley)Pinot GrisRainstorm
129969FranceA dry style of Pinot Gris, this is crisp with …NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970FranceBig, rich and off-dry, this is powered by inte…Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Car…GewürztraminerDomaine Schoffit

120975 rows × 13 columns

Assigning data

Going the other way, assigning data to a DataFrame is easy. You can assign either a constant value:

reviews['critic'] = 'everyone' reviews['critic']
0 everyone 1 everyone ... 129969 everyone 129970 everyone Name: critic, Length: 129971, dtype: object

Or with an iterable of values:

reviews['index_backwards'] = range(len(reviews), 0, -1) reviews['index_backwards']
0 129971 1 129970 ... 129969 2 129970 1 Name: index_backwards, Length: 129971, dtype: int64

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