How to get index of all rows whose particular column satisfies given condition in Pandas

  1. Simple indexing operation to get the index of all rows whose particular column satisfies given condition
  2. np.where() method to get index of all rows whose particular column satisfies given condition
  3. pandas.DataFrame.query() to get indices of all rows whose particular column satisfies given condition

We can get the index of all rows whose particular column satisfies given condition in Pandas using simple indexing operation. We could also find their indices using where() method from NumPy package and query() method of DataFrame object.

Simple indexing operation to get the index of all rows whose particular column satisfies given condition

The use of simple indexing operation can accomplish the task of getting the index of rows whose particular column meets the given condition.

import pandas as pd
import numpy as np

dates=['April-10', 'April-11', 'April-12', 'April-13','April-14','April-16']
sales=[200,300,400,200,300,300]
prices=[3, 1, 2, 4,3,2]

df = pd.DataFrame({'Date':dates ,
                   'Sales':sales ,
                   'Price': prices})

reqd_Index = df[df['Sales']>=300].index.tolist()
print(reqd_Index)

Output:

[1, 2, 4, 5]

Here, df['Sales']>=300 gives series of boolean values whose elements are True if their Sales column has a value greater than or equal to 300.

We can retrieve the index of rows whose Sales value is greater than or equal to 300 by using df[df['Sales']>=300].index.

Finally, the tolist() method converts all the indices to a list.

np.where() method to get index of all rows whose particular column satisfies given condition

np.where() takes condition as an input and returns the indices of elements that satisfy the given condition. Hence, we could use np.where() to get indices of all rows whose particular column satisfies the given condition.

import pandas as pd
import numpy as np

dates=['April-10', 'April-11', 'April-12', 'April-13','April-14','April-16']
sales=[200,300,400,200,300,300]
prices=[3, 1, 2, 4,3,2]

df = pd.DataFrame({'Date':dates ,
                   'Sales':sales ,
                   'Price': prices})

reqd_Index = list(np.where(df["Sales"] >= 300))
print(reqd_Index)

Output:

[array([1, 2, 4, 5])]

This outputs indices of all the rows whose values in the Sales column are greater than or equal to 300.

pandas.DataFrame.query() to get indices of all rows whose particular column satisfies given condition

pandas.DataFrame.query() returns DataFrame resulting from the provided query expression. Now, we can use the index attribute of DataFrame to return indices of all the rows whose particular column satisfies the given condition.

import pandas as pd
import numpy as np

dates=['April-10', 'April-11', 'April-12', 'April-13','April-14','April-16']
sales=[200,300,400,200,300,300]
prices=[3, 1, 2, 4,3,2]

df = pd.DataFrame({'Date':dates ,
                   'Sales':sales ,
                   'Price': prices})

reqd_index = df.query('Sales == 300').index.tolist()
print(reqd_index)

Output:

[1, 4, 5]

It returns the list of indices of all rows whose particular column satisfies the given condition Sales == 300.

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