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Articles tagged with #pandas
Original Dataset import pandas as pd import numpy as np # Step 1: Create a sample dataset data = { "A": [1, 2, np.nan, 4, 5], "B": [np.nan, 2, 3, np.nan, 5], "C": ["cat", "dog", np.nan, "cat", "dog"], "D": [10, 20, 30, 40, np.nan] } ...
1. Read Data from CSV, Excel, or JSON (pandas.read_csv, pandas.read_excel, pandas.read_json) Read CSV: import pandas as pd # Read data from CSV df = pd.read_csv("data.csv") print(df.head()) Output (Sample): ID Name Age 0 1 Alice 25 1 2 ...
1. Convert Column Names to Snake Case (pyjanitor.clean_names) import pandas as pd import janitor # Sample DataFrame df = pd.DataFrame({"Column Name 1": [1, 2], "AnotherColumn": [3, 4]}) # Convert column names to snake_case df = janitor.clean_names(...
1. Clean Messy Column Names (janitor.clean_names) import pandas as pd import janitor # Sample DataFrame df = pd.DataFrame({"Col 1 ": [1, 2], "COL@2": [3, 4]}) # Clean column names df = janitor.clean_names(df) print(df) Output: col_1 col_2 0 ...