Learn how to effectively remove unused categories from your Pandas DataFrame using the remove_unused_categories() method. Enhance your data analysis skills with this powerful technique.
Example 1: Drop Duplicates from pandas DataFrameIn this example, I’ll explain how to delete duplicate observations in a pandas DataFrame.For this task, we can use the drop_duplicates function as shown below:data_new1 = data.copy() # Create duplicate of example data data_new1 = data_new...
Python program to remove duplicate columns in Pandas DataFrame# Importing pandas package import pandas as pd # Defining two DataFrames df = pd.DataFrame( data={ "Parle": ["Frooti", "Krack-jack", "Hide&seek", "Frooti"], "Nestle": ["Maggie", "Kitkat", "EveryDay", "Crunch"], "...
Example 2: Remove Rows with NaN Values from pandas DataFrame This example demonstrates how to drop rows with any NaN values (originally inf values) from a data set. For this, we can apply the dropna function as shown in the following syntax: ...
How to Add Columns to a Pandas DataFrame Adding a column to a Pandas DataFrame is probably the easiest operation you can perform with a DataFrame. It actually doesn't require you to use any function, you only need to define the column name and the data that you want to store in that ...
The Python stringtranslate()method replaces each character in the string using the given mapping table or dictionary. Declare a string variable: s='abc12321cba' Copy Get the Unicode code point value of a character and replace it withNone: ...
technology = ["Python", "Spark", "Hadoop","Java", "Pandas"] print("Actual List: ",technology) # Using list.remove() function technology.remove(technology[0]) print("Final List: ",technology) Yields the same output as above. 5. Remove First Element from List Using Slicing ...
Method 8: Remove multiple characters from String using filter() function in Python Thefilter()function in Python allows for filtering elements of an iterable based on a function that returnsTrueorFalse. For Python strings, this can be used to filter out (or keep) specific characters. ...
For this, we can use the drop() function and the axis argument as shown below: data_new1=data.drop("x1",axis=1)# Apply drop() functionprint(data_new1)# Print updated DataFrame As shown in Table 2, the previous Python code has created a new pandas DataFrame with one column less, ...
…or the notnull function: data2c=data[pd.notnull(data["x2"])]# Apply notnull() functionprint(data2c)# Print updated DataFrame All the previous Python codes lead to the same output DataFrame. Example 3: Drop Rows of pandas DataFrame that Contain Missing Values in All Columns ...