🐼 20 of the most used Pandas + PDF functions
👨🏻💻 The first time I used Pandas, I was supposed to quickly clean and organize a raw and complex dataset with the help of Pandas functions. Using the groupby function, I was able to categorize the data and get in-depth analysis of customer behavior. Best of all, it was when I used loc and iloc that I could easily filter the data.
✔️ Since then I decided to prepare a list of the most used Pandas functions that I use on a daily basis. Now this list is ready! In the following, I will introduce 20 of the best and most used Pandas functions:
🏳️🌈 read_csv(): Fast data upload from CSV files
🏳️🌈 head(): look at the first five rows of the database to start..
🏳️🌈 info(): Checking data structure such as data type and empty values.
🏳️🌈 describe(): Generate descriptive statistics for numeric columns.
🏳️🌈 loc[ ]: accesses rows and columns by label or condition.
🏳️🌈 iloc[ ]: Access data by row number.
🏳️🌈 merge(): Merge dataframes with common columns.
🏳️🌈 groupby(): Grouping for easier analysis.
🏳️🌈 pivot_table(): Summarize data in pivot table format.
🏳️🌈 to_csv(): Save data as a CSV file.
🏳️🌈 pd.concat(): Concatenate multiple dataframes in rows or columns.
🏳️🌈 pd.melt(): Convert wide format data to long format.
🏳️🌈 pd.pivot_table(): Create a pivot table with multiple levels.
🏳️🌈 pd.cut(): Split the data into specific intervals.
🏳️🌈 pd.qcut(): Sort data by percentage.
🏳️🌈 pd.merge(): Merge data in database style for advanced linking.
🏳️🌈 DataFrame.apply(): Apply a custom function to the data.
🏳️🌈 DataFrame.groupby(): Analyze grouped data.
🏳️🌈 DataFrame.drop_duplicates(): Drop duplicate rows.
🏳️🌈 DataFrame.to_excel(): Save data directly to Excel file.
┌ 🐼 Pandas Functions
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