If you are looking to become an expert, check out my books:

• Exercise Python
• Master Data Analysis with Python
• Master Machine Learning with Python

They are all extremely comprehensive and offer lots of exercises with detailed solutions.

This notebook contains a summary of all the guidelines in this tutorial along with a list of attributes and methods that provide nearly all of the functionality of Pandas.

Minimally sufficient Pandas

• is simple, explicit, straightforward, and boring

• has one obvious way to accomplish a task

• uses this obvious way every single time

• is easier to retain in memory

• is easier to read and debug by yourself an others

• uses less of the library by eliminating methods that provide no additional functionality

• avoids Pandas bugs because of less code

• doesn't rely on being tricky to impress friends

• makes it easier to use in production

• Selecting Subsets of Data

  • Select a single column of data with the brackets
  • Do not use dot notation
  • Be explicit and use loc and iloc
  • Never use ix
  • No need to use at or iat

• Handling the SettingWithCopyWarning

  • Know the three cases when it appears
    • Correct assignment with side effects
    • No assignment
    • Correct assignment without side effects
  • To handle the warning, you will be in one of two scenarios
    • You want to work with a new independent DataFrame - use the copy method
    • You want to work with original DataFrame. Assign data with a single indexer, loc. Avoid chained indexing.

• Method Duplication

  • Many methods are aliases or provide no extra functionality. Only use one
  • All operators have methods. Only use methods when necessary
  • Always use Pandas methods and not builtin Python functions

• Say No to apply

  • apply is an automated for loop that passes each column or row to a user-defined function
  • Use apply as a method of last resort
  • Using apply with axis='columns' is one of the slowest operations in all of Pandas

• Standardizing groupby

  • Know the three components
    • Grouping columns
    • Aggregating columns
    • Aggregating functions
  • Use the syntax `df.groupby('grouping columns').agg({'aggregating column': 'aggregating function'})

• Handling a MultiIndex

  • A MultiIndex is difficult to make selections and further process
  • I suggest having a single level index
  • Rename the columns and reset the index after a groupby

• Say no to apply with groupby

  • Can be extremely slow to use apply with groupby
  • Call all methods independent of the group, outside of the custom function

• Similarity between groupby, pivot_table, crosstab

• Similarity between melt, pivot, stack, unstack

Below is a short list of DataFrame attributes and methods that allows you maximum coverage of the library.

• T
• abs
• all
• any
• append
• asfreq
• astype
• clip
• columns
• copy
• corr
• count
• cov
• cummax
• cummin
• cumprod
• cumsum
• describe
• diff
• drop
• drop_duplicates
• dropna
• dtypes
• equals
• expanding
• fillna
• groupby
• head
• idxmax
• idxmin
• iloc
• index
• interpolate
• isin
• isna
• loc
• max
• mean
• median
• melt
• merge
• min
• mode
• nlargest
• notna
• nsmallest
• nunique
• pct_change
• pivot_table
• plot
• prod
• quantile
• rank
• rename
• replace
• resample
• reset_index
• rolling
• round
• sample
• select_dtypes
• shape
• shift
• sort_index
• sort_values
• std
• sum
• tail
• to_csv
• to_sql
• values
• var