You can call groupby() and pass the name of the column that you want to group on in Pandas. Then, you need to specify the columns on which you want to perform the aggregation. The following is the basic syntax grammar. df.groupby(“column_name1”)[“column_name2”].function The following is to generate the sample dataframe. Brand Location Number 0 Brand … Read more
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 1 CA 20 2 Brand 2 CA 300 3 Brand 1 NY 400 4 Brand … Read more
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 and a matrix first. The following is the print out of the dataframe. Dataframe: Brand … Read more
You can add a NumPy array as a new column to Pandas dataframes by using the tolist() function. The following are the syntax statement as well as examples showing how to actually do it. df[‘new_column_name’] = array_name.tolist() Step 1: Generate a sample dataframe The following is to generate a dataframe and then print it out. … Read more
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 only one expression, typically in one-line expression. The following is an example of adding the number … Read more
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 in Pandas Brand Location Year DateTime 0 Tesla CA 2019 2019-03-10 1 Tesla CA 2018 … Read more
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 least have 1 NaN. The following is an example. The following shows the original dataframe … Read more
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 dataframe in Python. The following is the sample dataframe to be used later. Brand Location … Read more
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 10 50.5 2 50 88.0 3 30 99.0 The following is to use the function … Read more
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 2010 6 Toyota CA 2018 7 Toyota TX 2021 Car_Brand Location Year 0 Tesla CA … Read more