Extracting Data from CSV and JSON Files
12345import pandas as pd # Read a CSV file and display its contents df = pd.read_csv("data/sample_data.csv") print(df.head())
Reading data from CSV files is a common task in data pipelines. You use the read_csv function from the pandas library to load the file into a DataFrame. This function automatically detects the delimiter (default is comma), but you can specify a different delimiter using the delimiter or sep parameter if your file uses something else, such as a tab or semicolon. File encoding is another important aspect; most CSV files use UTF-8 encoding, but you might encounter files with different encodings like ISO-8859-1. You can specify the encoding with the encoding parameter. If you try to read a file with the wrong encoding, you may see errors or garbled text. Error handling is crucial during extraction. The read_csv function provides options like error_bad_lines=False (deprecated in newer pandas versions) or on_bad_lines="skip" to skip problematic rows, and warn_bad_lines=True to display warnings. Always check the documentation for your pandas version to ensure you use the correct parameters.
123456789101112import pandas as pd # Read a JSON file with nested structures df = pd.read_json("data/nested_data.json") # If the JSON file contains deeply nested data, use json_normalize if "records" in df.columns: from pandas import json_normalize nested_df = json_normalize(df["records"]) print(nested_df.head()) else: print(df.head())
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How can I handle errors when reading CSV files with pandas?
Can you explain how to specify a different delimiter or encoding in read_csv?
What should I do if my JSON file has multiple levels of nesting?
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12345import pandas as pd # Read a CSV file and display its contents df = pd.read_csv("data/sample_data.csv") print(df.head())
Reading data from CSV files is a common task in data pipelines. You use the read_csv function from the pandas library to load the file into a DataFrame. This function automatically detects the delimiter (default is comma), but you can specify a different delimiter using the delimiter or sep parameter if your file uses something else, such as a tab or semicolon. File encoding is another important aspect; most CSV files use UTF-8 encoding, but you might encounter files with different encodings like ISO-8859-1. You can specify the encoding with the encoding parameter. If you try to read a file with the wrong encoding, you may see errors or garbled text. Error handling is crucial during extraction. The read_csv function provides options like error_bad_lines=False (deprecated in newer pandas versions) or on_bad_lines="skip" to skip problematic rows, and warn_bad_lines=True to display warnings. Always check the documentation for your pandas version to ensure you use the correct parameters.
123456789101112import pandas as pd # Read a JSON file with nested structures df = pd.read_json("data/nested_data.json") # If the JSON file contains deeply nested data, use json_normalize if "records" in df.columns: from pandas import json_normalize nested_df = json_normalize(df["records"]) print(nested_df.head()) else: print(df.head())
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