Selecting rows in pandas DataFrame based on conditions

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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.

Python3


# 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(&quot;Given Dataframe :\n&quot;, dataframe) 

# selecting rows based on condition
rslt_df = dataframe[dataframe['Percentage'] &gt; 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.

Python3


# 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(&quot;Given Dataframe :\n&quot;, dataframe) 

# selecting rows based on condition
rslt_df = dataframe.loc[dataframe['Percentage'] &gt; 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.

Python3


# 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(&quot;Given Dataframe :\n&quot;, 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.

Python3


# 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(&quot;Given Dataframe :\n&quot;, dataframe) 

options = ['Math', 'Commerce']

# selecting rows based on condition
rslt_df = dataframe[dataframe['Stream'].isin(options)]

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

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)

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)

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)

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

# 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)