df.assign allows you to assign values to multiple new columns in one line of code.
In the code above, I first created col3 then usecol3 to create col4 in one line of code.
Assign Values to Multiple Columns in a pandas DataFrame Read More »
If you want to increase the readability of your code when applying multiple functions to a DataFrame, use the pipe method.
pipe: Increase the Readability when Applying Functions to a DataFrame Read More »
If you want to get the difference between rows of a pandas DataFrame, use df.diff().Â
Adjust the periods argument to shift more than one period for calculating the difference.Â
Calculate the Difference Between Rows of a pandas DataFrame Read More »
If you want to filter a pandas DataFrame based on the index’s name, you can use either filter or loc.
Filter a pandas DataFrame Based on Index’s Name Read More »
If you want to show and align the differences between two DataFrames, use df.compare.
Compare the Difference Between Two DataFrames Read More »
If you want to get the n largest values for each category in a pandas DataFrame, use the combination of groupby and nlargest.
Get the N Largest Values for Each Category in a DataFrame Read More »
If you want to take the sum of all columns, use sum(axis=1). If you want to take the sum of all rows, use sum(axis=0).
Get the Sum of All Columns in a pandas DataFrame Read More »
To find pandas DataFrame whose columns start with a pattern, use df.columns.str.startswith.
DataFrame.columns.str.startswith: Find DataFrame’s Columns that Start With a Pattern Read More »
There are multiple ways for you to encode a categorical column in a pandas DataFrame. If you simply want to convert each value in a column to a number, use cat.codes.
Encode Categorical Values in a Pandas DataFrame Read More »
If you want to replace missing values with the most frequent categories in a column, find the mode of your DataFrame then use it to fill the missing values.
Replace the Missing Values with the Most Frequent Categories in a Column Read More »