Top 20 Pandas functions for exploratory data analysis

📚 Top functions which are useful while doing exploratory data analysis
Author

Parag Panchal

Published

November 1, 2023



Pandas is a popular 🐍Python library for data analysis, and it provides a wide range of tools and functions for working with structured data. One of the most common tasks in data analysis is exploratory data analysis (EDA), which involves summarizing and visualizing data to gain insights and identify patterns. In this article, we’ll take a look at the top 20 Pandas functions which are commonly used for EDA.

  1. read_csv(): This function is used to read data from a CSV file and create a Pandas DataFrame.
  2. head(): This function is used to display the first few rows of a DataFrame.
  3. info(): This function is used to display information about the DataFrame, including the data types of each column and the number of non-null values.
  4. describe(): This function is used to generate summary statistics for the DataFrame, including measures of central tendency and dispersion.
  5. value_counts(): This function is used to count the number of occurrences of each unique value in a column.
  6. isnull(): This function is used to identify missing values in a DataFrame.
  7. dropna(): This function is used to remove rows or columns with missing values from a DataFrame.
  8. fillna(): This function is used to replace missing values with a specified value or method.
  9. groupby(): This function is used to group data by one or more columns and perform operations on the resulting groups.
  10. pivot_table(): This function is used to create a pivot table, which summarizes data by grouping it according to two or more variables.
  11. merge(): This function is used to combine two or more DataFrames based on a common column or index.
  12. apply(): This function is used to apply a function to each row or column of a DataFrame.
  13. astype(): This function is used to convert the data type of a column to a different type.
  14. plot(): This function is used to create various types of plots, including line, scatter, and bar charts.
  15. corr(): This function is used to compute the correlation matrix between columns of a DataFrame.
  16. crosstab(): This function is used to create a contingency table, which summarizes the frequency of occurrences of two categorical variables.
  17. quantile(): This function is used to compute the quantiles of a column, which can be used to identify outliers.
  18. idxmax(): This function is used to identify the index of the maximum value in a column.
  19. nlargest(): This function is used to return the n largest values in a column.
  20. nsmallest(): This function is used to return the n smallest values in a column.

In conclusion, Pandas provides a wide range of functions for exploratory data analysis, including reading and summarizing data, identifying missing values, grouping and aggregating data, and creating visualizations. By using these functions effectively, data analysts can gain insights and identify patterns in their data, which can inform decision-making and drive business value. While there are many more functions available in Pandas, these 20 functions represent some of the most commonly used and powerful tools for EDA.

Back to top