Selecting rows in pandas DataFrame based on conditions

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.

# 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[].

# 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'] > 80]

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

Output :

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

# 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.

# 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[].

# 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[].

# 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.

# 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[].

# 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 :