Category: Python

Introduction of crosstab() function You can use the pandas.crosstab() function to create a contingency table. It computes a simple cross tabulation of two (or more) factors. The following is the sample data Brand Location Number 0 Brand 1 CA 200 1 Brand...

You can combine Pandas dataframes and Numpy Matrices by using the pd.concat() function in Pandas. pd.concat([df,pd.DataFrame(Matrix)],axis=1) The following are the steps to combine Pandas dataframe and Numpy matrix. Step 1: Generate a dataframe The following is to generate a dataframe...

This short tutorial aims to show how you can use Lambda functions in Python, and especially in Pandas. Introduction The following is the basic structure of Lambda functions: lambda bound_variable: function_or_expression Lambda functions can have any number of arguments but...

You can use the function of dtype() to check the data type of columns for Pandas dataframes. You can either check a single column or all the columns. The following is the sample code. Check Data Type for All Columns...

You can drop rows or columns with missing data (e.g., with NaN) using dropna() in Pandas. Drop rows with NaN: df.dropna() Drop columns with NaN: df.dropna(axis=”columns”) Example of dropping rows with NaN By default, dropna() will drop rows that at...

To get frequency counts of a column in Pandas, you can use the function of value_counts() or groupby().size(). The following shows two actual method examples. Method 1: df[“column_name”].value_counts() Method 2: df.groupby([“column_name”]).size() Data Example The following is to generate a sample...

Method 1: Mean centering just one column in a dataframe You can use mean() function to do mean centering for one column in dataframes in Python Pandas. Below, we generate a sample data first. Col_1 Col_2 0 20 50.0 1...

You can rename just one column using the rename() function in Python Pandas dataframes. Brand Location Year 0 Tesla CA 2019 1 Tesla CA 2018 2 Tesla NY 2020 3 Ford MA 2019 4 Ford CA 2016 5 Ford WA...

Methd 1: Use len() You can use len(df.index) to check the number of row in Python Pandas dataframes. Brand Location Year 0 Tesla CA 2019 1 Tesla CA 2018 2 Tesla NY 2020 3 Ford MA 2019 4 Ford CA...