Selecting DataFrame Rows

Table of Contents

• Introduction
• Selection by the loc Attribute
  • Selecting a Cell
  • Selecting a Row
  • Selecting Multiple Rows
• Selection by the iloc Attribute
  • Selecting a Cell
  • Selecting a Row
  • Selecting Multiple Rows

Introduction

In this section, we will discuss accessing the rows in a dataframe by both label (loc attribute) and position (iloc attribute).

We first import the required libraries and modules.

1import numpy as np
2import pandas as pd
3import random as rd

The next step is to create a DataFrame from a dictionary of lists with the help of the random module.

1a=['Tom','Gary','Lois','Wendy','Betty']
2b=rd.choices(range(50,85),k=5)
3c=np.random.uniform(1.6, 1.9, 5).round(2)
4
5df = pd.DataFrame({'Weight':b,
6                   'Height':c}, index=a)
7df

Selection by the loc Attribute

Syntax

1dataframe.loc[row_selection, column_selection]

where row_selection and column_selection refer to a list of row labels and column labels, respectively, i.e. ['label1', 'label2', 'label3']. loc supports the slice notation and therefore accepts a colon(:) to select all rows or columns.

Selecting a Cell

We can select a cell value by specifying both row and column labels.

Example

1df.loc['Tom','Height']

1.65

Selecting a Row

Select an entire row using its label. The colon (:) for column_selection indicates all columns are selected.

Example

1df.loc['Tom',:]

Weight    58.00
Height     1.65
Ratio     35.20
Name: Tom, dtype: float64

The above returns a Series object.

1type(df.loc['Tom',:])

pandas.core.series.Series

If the row label is in a list, then a DataFrame will be created instead.

Example

1df.loc[['Tom'],:]

1type(df.loc[['Tom'],:])

pandas.core.frame.DataFrame

It is also possible to select a row using the following shorter version by omitting the comma and colon.

Example

1df.loc['Gary']

Weight    54.00
Height     1.84
Ratio     29.30
Name: Gary, dtype: float64

Selecting Multiple Rows

The labels of the rows we wish to select are inside a list. This will always return a DataFrame.

Example

1df.loc[['Tom','Betty'],:]

We can also selecting multiple rows by omitting the comma and colon.

Example

1df.loc[['Gary','Tom']]

We can also select multiple contiguous rows (adjacent to each other with no gap) using the colon : notation.

Example

1df.loc['Gary':'Wendy',:]

Example

1df.loc['Gary':'Betty']

Selection by the iloc Attribute

Syntax

1dataframe.iloc[row_selection, column_selection]

where row_selection and column_selection refer to a list of row indices and column indices, respectively.iloc supports the slice notation and therefore accepts a colon(:) to select all rows or columns.

Selecting a Cell

We can select a cell value by specifying both row and column indices.

Example

1df.iloc[0,0]

58

Selecting a Row

Select an entire row using its label. The colon (:) in column_selection indicates all columns are selected.

Example

1df.iloc[1,:]

Weight    54.00
Height     1.84
Ratio     29.30
Name: Gary, dtype: float64

The above returns a Series object.

1type(df.iloc[1,:])

pandas.core.series.Series

If the row index is in a list, then a DataFrame will be created instead.

Example

1df.iloc[[1],:]

1type(df.iloc[[1],:])

pandas.core.frame.DataFrame

Selecting Multiple Rows

Selecting multiple rows (whose indices are inside a list) will always return a DataFrame.

Example

1df.iloc[[1,3],:]

We can also select multiple contiguous rows (adjacent to each other with no gap) using the slice (colon :) notation.

Example

1df.iloc[1:3,:]