# Get Index of All Rows Whose Particular Column Satisfies Given Condition in Pandas

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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