Selecting rows in pandas DataFrame based on conditions (original) (raw)

Last Updated : 07 Aug, 2024

Let’s see how to Select rows based on some conditions in Pandas DataFrame.

Selecting rows based on particular column value using '>', '=', '=', '<=', '!=' operator.

Code #1 : Selecting all the rows from the given dataframe in which ‘Percentage’ is greater than 80 using basic method.

Python `

importing pandas

import pandas as pd

record = {

'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ], 'Age': [21, 19, 20, 18, 17, 21], 'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage': [88, 92, 95, 70, 65, 78] }

create a dataframe

dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", dataframe)

selecting rows based on condition

rslt_df = dataframe[dataframe['Percentage'] > 80]

print('\nResult dataframe :\n', rslt_df)

`

**Output :

Code #2 : Selecting all the rows from the given dataframe in which ‘Percentage’ is greater than 80 using loc.

Output :

Code #3 : Selecting all the rows from the given dataframe in which ‘Percentage’ is not equal to 95 using Loc.

Python `

importing pandas

import pandas as pd

record = { 'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ], 'Age': [21, 19, 20, 18, 17, 21], 'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage': [88, 92, 95, 70, 65, 78]}

create a dataframe

dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", dataframe)

selecting rows based on condition

rslt_df = dataframe.loc[dataframe['Percentage'] != 95]

print('\nResult dataframe :\n', rslt_df)

`

Output :

Selecting those rows whose column value is present in the list using isin() method of the dataframe.

Code #1 : Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using basic method.

Python `

importing pandas

import pandas as pd

record = { 'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ], 'Age': [21, 19, 20, 18, 17, 21], 'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage': [88, 92, 95, 70, 65, 78]}

create a dataframe

dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", dataframe)

options = ['Math', 'Commerce']

selecting rows based on condition

rslt_df = dataframe[dataframe['Stream'].isin(options)]

print('\nResult dataframe :\n', rslt_df)

`

Output :

Code #2 :

Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using .loc[].

Python `

importing pandas

import pandas as pd

record = { 'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ], 'Age': [21, 19, 20, 18, 17, 21], 'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage': [88, 92, 95, 70, 65, 78]}

create a dataframe

dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", dataframe)

options = ['Math', 'Commerce']

selecting rows based on condition

rslt_df = dataframe.loc[dataframe['Stream'].isin(options)]

print('\nResult dataframe :\n', rslt_df)

`

Output :

Code #3 : Selecting all the rows from the given dataframe in which ‘Stream’ is not present in the options list using .loc[] .

Python `

importing pandas

import pandas as pd

record = { 'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ], 'Age': [21, 19, 20, 18, 17, 21], 'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage': [88, 92, 95, 70, 65, 78]}

create a dataframe

dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", dataframe)

options = ['Math', 'Science']

selecting rows based on condition

rslt_df = dataframe.loc[~dataframe['Stream'].isin(options)]

print('\nresult dataframe :\n', rslt_df)

`

Output :

Selecting rows based on multiple column conditions using '&' operator.

Code #1 : Selecting all the rows from the given dataframe in which ‘Age’ is equal to 21 and ‘Stream’ is present in the options list using basic method.

Python `

importing pandas

import pandas as pd

record = { 'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ], 'Age': [21, 19, 20, 18, 17, 21], 'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage': [88, 92, 95, 70, 65, 78]}

create a dataframe

dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", dataframe)

options = ['Math', 'Science']

selecting rows based on condition

rslt_df = dataframe[(dataframe['Age'] == 21) & dataframe['Stream'].isin(options)]

print('\nResult dataframe :\n', rslt_df)

`

Output :

Code #2 :

Selecting all the rows from the given dataframe in which ‘Age’ is equal to 21 and ‘Stream’ is present in the options list using .loc[].

Python `

importing pandas

import pandas as pd

record = { 'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ], 'Age': [21, 19, 20, 18, 17, 21], 'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage': [88, 92, 95, 70, 65, 78]}

create a dataframe

dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", dataframe)

options = ['Math', 'Science']

selecting rows based on condition

rslt_df = dataframe.loc[(dataframe['Age'] == 21) & dataframe['Stream'].isin(options)]

print('\nResult dataframe :\n', rslt_df)

`

Output :