Boost Your Exploratory Data Analysis with Pandas Profiling

  • Basic data type information (which columns contain what)
  • Descriptive statistics (mean, average, etc.)
  • Quantile statistics (tells you about how your data is distributed)
  • Histograms for your data (again, for visualizing distributions)
  • Correlations (Let’s you see what’s related)
  • And more!
pip install pandas-profiling[notebook]
conda install -c conda-forge pandas-profiling
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
df = pd.read_csv('my_data.csv')
profile = ProfileReport(df, title="Pandas Profiling Report")

Saving the report

profile.to_file("your_report.html")
# As a string
json_data = profile.to_json()
# As a file
profile.to_file("your_report.json")

Large datasets

profile = ProfileReport(large_dataset, minimal=True)
profile.to_file("output.html")

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