- Pandas is a very handy and powerful python library for handling data frames and various tedious tasks as hand. Link1 Link2
- Boolean-Indexing
- Series.map
- Working-with-text-data
- Value-Counts more
- Indexing and Selecting data
- Apply, Map
- Group-by more
- markdown cell
- Python graph gallery for matplotlib and seaborn
- Working with matplotlib
- Here is a Reference link to plotting with categorical data
- The first thing to do is to always Identify the missing values within the dataset. The few steps after this explain how to deal with the missing data
- If there are columns with a few rows of missing data the Dropna method could be used to drop the missing rows.
- If there are rows with missing data the Fillna-method can be used instead of dropping them completely (This method can vary with the data and the project)
- The final option is if there are way too many missing values within a column it is best to drop the column completely using the Drop-column-method
- Binning or Cutting Groups continuous or numerical values into smaller groups or ‘bins’
- Pandas-Dummies Transforms categorical data into dummy/indicator variables
Investigate a Dataset
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