My Python Cheat Sheet

These are some python code snippets that I use very often.

List Manipulation

Concatenate two python lists


Convert a python string to a list of characters


JSON Manipulation

Convert a dictionary to a json string


Convert a json string back to a python dictionary


Load a json file into a pandas data frame


DataFrame Manipulation

Group by a column and keep the column afterwards


Convert a dictionary to a pandas data frame

Let’s say you have a dict as follows:


To convert this to a pandas Data Frame, you can do the following:

You will see the following output:


Select rows matching a specific column criteria

Let’s say you want to find rows where the column value matches a specific constraint. You could use the following:


Sort data frame by value


Create a new derived column in your data frame

The goal here is to create a new column with values populated based on the values of an old column. Let’s say you want a new column that adds 1 to a value from an old column.


Select specific columns from a data frame


System Commands

Run a system command from within Python code


File / Directory Operations

Safely create nested directories in Python



Compute per-class precision, recall, f1 scores

The goal here is to compute per-class precision, recall and f1 scores and display the results using a data frame.

The first step is to collect your labels as two separate lists. (1) the predicted labels and (2) the corresponding true labels. For example:

Once you have the true and predicted labels in a list, you can use sklearn’s precision_recall_fscore_support module to compute all the scores for you. Here’s how you do it:

Example output: