this is my pandas

1. Quick Examples of Drop Rows With Condition in Pandas If you are in a hurry, below are some quick examples of pandas dropping/removing/deleting rows with condition(s).

Quick Examples

Using drop() to delete rows based on column value

df.drop(df[df['Fee'] >= 24000].index, inplace = True)

Remove rows

df2 = df[df.Fee >= 24000]

If you have space in column name

Specify column name with in single quotes

df2 = df[df['column name']]

Using loc

df2 = df.loc[df["Fee"] >= 24000 ]

Delect rows based on multiple column value

df2 = df[ (df['Fee'] >= 22000) & (df['Discount'] == 2300)]

Drop rows with None/NaN

df2 = df[df.Discount.notnull()] Python Let’s create a DataFrame with a few rows and columns and execute some examples to learn how to drop the DataFrame rows. Our DataFrame contains column names Courses, Fee, Duration, and Discount.

Create pandas DataFrame

import pandas as pd import numpy as np technologies = { 'Courses':["Spark","PySpark","Hadoop","Python"], 'Fee' :[22000,25000,np.nan,24000], 'Duration':['30day',None,'55days',np.nan], 'Discount':[1000,2300,1000,np.nan] } df = pd.DataFrame(technologies) print(df) Python Yields below output.

Courses Fee Duration Discount 0 Spark 22000.0 30day 1000.0 1 PySpark 25000.0 None 2300.0 2 Hadoop NaN 55days 1000.0 3 Python 24000.0 NaN NaN Bash 2. Using DataFrame.drop() to Drop Rows with Condition drop() method takes several params that help you to delete rows from DataFrame by checking condition. When condition expression satisfies it returns True which actually removes the rows.

df.drop(df[df['Fee'] >= 24000].index, inplace = True) print(df) Python Yields below output.

Courses Fee Duration Discount 0 Spark 22000.0 30day 1000.0 2 Hadoop NaN 55days 1000.0 Bash After removing rows, it is always recommended to reset the row index.

1. Using loc[] to Drop Rows by Condition