Pandas Tutorial

pandas is a library that provides very user-friendly tools for storing and working with data. If you are doing data analysis or machine learning and at the same time using the Python language , then you simply must know and be able to work with pandas .

pandas is part of a group of projects sponsored by numfocus . Numfocus is an organization that supports various projects related to scientific computing.

pandas is very fast, flexible and expressive and perfect for working with one-dimensional and two-dimensional data tables, it is well integrated with the outside world – it is possible to work with CSV files , Excel tables , and can interface with the R language.

Section 1. Getting Started with Pandas

Section 2. Fundamentals of Pandas

Section 3. Creating DataFrame

Section 4. Basic DataFrame Operations

Section 5. Adding data

  • Add DataFrame rows – learn how to use concat() and append() to add rows to a DataFrame.
  • append()
  • insert()
  • concat()
  • merge()
  • join()

Section 6 : Editing data

  • where()
  • melt()
  • cut()

Section 7 : Deleting data

  • drop()
  • del

Section 8. Data Cleaning and Prepping

  • Handling missing data – learn how Pandas treat empty value internally.
  • dropna() – learn how to use dropna() to filter out empty rows and columns.
  • fillna() – learn how to use fillna() to fill in custom values in position of NaN values.
  • drop_duplicates() – the method that allows you how to remove duplicate rows based on conditions.
  • map() – show you how to transform the data using mappings
  • replace() – a nifty function that replaces a value with another.
  • Rename DataFrame column names and row indexes.
  • String manipulation – show you how to apply string operations on whole arrays of data.
  • Vectorized string functions – apply string and regex operations through the built-in array-optimized string methods.

Section 9. Group/Groupby

  • Create a group
  • Group operations
  • Sorting groups
  • Transform groups

Section 10. Pivot and Pivot Table

  • pivot()
  • pivot_table()
  • Multilevel columns
  • Data for selected value
  • Duplicate values
  • Customize missing values

Section 11. Data Transformation with pandas

  • Stack and unstack
  • Melt
  • Transpose

Section 12. Working with date and time

  • pandas DatetimeIndex
  • Date ranges
  • Custom holidays
  • Date formats
  • Timespan/Period
  • Time zones
  • Shift dates and times