• Descriptive Statistics
  • Data Visualization with Pandas
  • Handling Missing Data
  • Working with Dates and Times
  • Merging and Joining DataFrames

Pandas Data validation

Data validation is an essential step in any data analysis or machine learning project. It involves checking data quality, consistency, and correctness to ensure that the data is reliable and suitable for the intended analysis or modeling. Pandas provides several functions and tools for data validation, such as checking for missing values, checking for duplicates, checking data types, and more. Here are some common data validation tasks in Pandas: (more…)

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How to subtract dates in Pandas

In Pandas, you can subtract two dates to get the time delta between them. The result will be a Timedelta object, which represents the difference between two dates or times in terms of days, seconds, microseconds, milliseconds, minutes, hours, weeks, or years.

For example, consider the following two dates: (more…)

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How to VLOOKUP in Pandas

I try to show you how to vlookup in Pandas.

The VLOOKUP function in Excel is a popular function that allows you to search for a value in a table and return the corresponding value from another column. The equivalent of a VLOOKUP in Pandas is the merge method, which allows you to join two DataFrames based on a common column.

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