Python Pandas Crash Course: Analyze Data in 20 Minutes Pandas is the most popular Python library for data analysis. Here's what you need to know. Load and Inspect Data import pandas as pd df = pd . read_csv ( ' sales.csv ' , parse_dates = [ ' date ' ]) print ( df . shape ) # (rows, cols) print ( df . info ()) # Column types, null counts print ( df . describe ()) # Statistical summary print ( df . head ( 5 )) # First 5 rows Enter fullscreen mode Exit fullscreen mode Filter and Select # Filter rows high_sales = df [ df [ ' revenue ' ] > 1000 ] q4 = df [ df [ ' date ' ]. dt . quarter == 4 ] # Select columns subset = df [[ ' product ' , ' revenue ' , ' date ' ]] # Multiple conditions result = df [( df [ ' revenue ' ] > 500 ) & ( df [ ' status ' ] == ' completed ' )] Enter fullscreen mode Exit fullscreen mode Group and Aggregate # Monthly sales summary monthly = df . groupby ( df [ ' date ' ]. dt . to_period ( ' M ' )). agg ({ ' revenue ' : ' sum ' , ' orders ' : ' count ' , ' profit ' : ' mean ' }).…