This is to get started with pandas and try few concrete examples. pandas is a Python based library that helps in reading, transforming, cleaning and analyzing data. It is built on the NumPy package.

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

https://pandas.pydata.org

Key data structure in pandas is called DataFrame – it helps to work with tabular data translated as rows of observations and columns of features.

Download or fork entire Jupiter notebook from my GitHub to play around: https://github.com/sandeep-mewara/python-examples

pandas basics includes:

• Series
• Dataframes
  • Create
    • from list of tuples
    • from a dictionary
    • from a CSV
    • from built-in dataset (eg: from sklearn.datasets)
  • Data retrieval
  • Modifying data
  • Group by operation
  • Custom Functions – apply method
  • Pre-Processing
    • drop, mean, mode
    • ordinal feature
    • nominal feature
  • Reshaping
    • CrossTab
    • Merge
    • Melt
    • Pivot

# .info(), .head(), .sample are handy method to use first off with dataframe to get a high level details

# index may be not unique – can return multiple values

# boolean indexing (masking) can help select certain set of rows

# .isin() is a useful when building a boolean index

# .where() is useful to retain shape of the original table

# Column names & Indexes can be set if needed

# to modify the table right away, use inplace=True

# aggregate operations can be applied on a groupby object

# dropna(), mean() or mode() are handy ways for pre-processing missing data

Key learning’s …

Examples notebook includes:

• Uber taxi drivers
• Apple stock price
• Day or Night
• Students marks
• Balance Calculator

# .describe() is a handy method to get the statistical summary of numerical columns

# one-hot-encoding is really helpful for nominal features (that cannot be ordered)

# converting the columns into right datatype helps

# converting data into meaningful numbers help for analysis

# groupby is a powerful tool with dataframes for analysis

Key learning’s …

Cheat sheet

Download cheat sheet pdf from here
For more details about pandas, look at the documentation reference.

Keep learning!