🐼 Pandas Data Indexing¶

Indexing is a technique in pandas that allows you to access or modify the data in your DataFrame or Series. This tutorial will walk you through the basics of indexing in pandas.

1. Importing the Pandas library¶

First, you need to import the pandas library, typically done under the pd alias.

In [1]:

import pandas as pd

import pandas as pd

2. Creating a DataFrame¶

Let's create a simple DataFrame for this tutorial:

In [2]:

data = {
    'name': ['John', 'Anna', 'Peter', 'Linda'],
    'age': [28, 24, 35, 32],
    'city': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)
df

data = { 'name': ['John', 'Anna', 'Peter', 'Linda'], 'age': [28, 24, 35, 32], 'city': ['New York', 'Paris', 'Berlin', 'London'] } df = pd.DataFrame(data) df

Out[2]:

	name	age	city
0	John	28	New York
1	Anna	24	Paris
2	Peter	35	Berlin
3	Linda	32	London

3. Column Indexing¶

You can index a column in a DataFrame using its column label with a syntax similar to dictionary key indexing:

In [3]:

df['name']

df['name']

Out[3]:

0     John
1     Anna
2    Peter
3    Linda
Name: name, dtype: object

You can also use the . (dot) operator, which is more convenient but only works if the column name doesn't contain spaces or special characters and doesn't conflict with DataFrame methods:

In [4]:

df.name

df.name

Out[4]:

0     John
1     Anna
2    Peter
3    Linda
Name: name, dtype: object

4. Row Indexing¶

For row indexing, you use the loc and iloc indexer.

loc is label-based data selection method which means that we have to pass the name of the row or column which we want to select. This method includes the last element of the range passed in it, unlike iloc.

In [5]:

df.loc[0]  # returns the first row

df.loc[0] # returns the first row

Out[5]:

name        John
age           28
city    New York
Name: 0, dtype: object

iloc is a indexed-based selection method which means that we have to pass integer index in the method to select specific row/column. This method does not include the last element of the range passed in it unlike loc.

In [6]:

df.iloc[0]  # returns the first row

df.iloc[0] # returns the first row

Out[6]:

name        John
age           28
city    New York
Name: 0, dtype: object

Note that the two commands above return the same result because the DataFrame uses integer indices. If the DataFrame used string labels for indices, the results could be different.

5. Multi-axis Indexing¶

You can use loc and iloc to index both rows and columns at the same time. The syntax is df.loc[[row_indexer], [col_indexer]].

In [7]:

df.loc[0, 'name']  # returns the name of the first person

df.loc[0, 'name'] # returns the name of the first person

Out[7]:

'John'

With iloc, you'd use integer indices for both the row and column:

In [8]:

df.iloc[0, 0]  # returns the name of the first person

df.iloc[0, 0] # returns the name of the first person

Out[8]:

'John'

6. Boolean Indexing¶

You can also use a condition to index data. This is very useful when you want to filter rows that satisfy a certain condition:

In [9]:

df[df.age > 30]  # returns people older than 30

df[df.age > 30] # returns people older than 30

Out[9]:

	name	age	city
2	Peter	35	Berlin
3	Linda	32	London

7. Setting Indices¶

DataFrame has a set_index method which takes a column name (or a list of column names) to use as the index of the DataFrame:

In [10]:

df.set_index('name', inplace=True)
df

df.set_index('name', inplace=True) df

Out[10]:

	age	city
name
John	28	New York
Anna	24	Paris
Peter	35	Berlin
Linda	32	London

Now you can use loc with people's names:

In [11]:

df.loc['John']

df.loc['John']

Out[11]:

age           28
city    New York
Name: John, dtype: object

This concludes our brief tutorial on the basics of pandas indexing. With these indexing techniques, you can effectively manipulate and access data in your DataFrame.