I plan to review the following topics that I supposed are the minimal for a job interview.
| Topic | Link | Functions of Interest |
|---|---|---|
| Introduction to Pandas: Series, DataFrame, and Basics | |
Data Structures: pd.Series(), pd.DataFrame() Handy Functions: df.to_numpy(), df.index, df.columns, df.set_index(), df.reset_index(), df.rename() Iteration: items(), iterrows(), itertuples() |
| Data Manipulation: Indexing, Slicing, and Filtering | |
Indexing and Slicing: df[<column_name>],.loc[], .iloc[] Filtering: conditional indexing (as numpy) Conditional Filling Data: .where() |
| Data Manipulation: Multi-index | |
Multi-index Structure: index, columns, axis, and levels Creation: .from_arrays(), .from_tuples(), .from_product() Indexing and Slicing: .loc[<tuple1>,<tuple2>] .loc[<list of tuples>] (or slicing), .loc[<tuple of lists>] .loc[<label_on_first_level>],df.loc(axis = 0)[], .xs() Functions of Interest: set_index() and reset_index() can work together reindex() swaplevel() and reorder_levels() rename, rename_axis, index.set_names, .set_axis() |
| Data Manipulation: Inserting, Deleting, Sorting | |
Inserting: using accesor, .insert() Deleting: .drop(), .del, .pop() Sorting: .sort_index(), .sort_values() |
| Data Manipulation: Concatenation, Merging and Compare | Concatenation: concat() (multiple parameters) Merging: merge(), join() (multiple parameters) Comparing: compare() |
|
| Grouping Operations: Aggregation, Transformation and Filtering | |
Splitting: groupby() Applying: aggregate(), transform(), filter(), apply() (can use built-in and UDF functions) |
| Data Manipulation: Reshaping | |
pivot(), pivot_table(), stack(), unstack(), melt() |
| Windowing Operations | |
.rolling(), .expanding(), .ewm() Properties: min_periods, center, closed, .apply(), corr(), cov() |
| Data Cleaning: Handling Missing Data (Optional) | ||
| Time Series Analysis (Optional) | ||
| Data Manipulation: Arithmetic Operations (Optional) | ||
| Performance Optimization (Optional) | ||
| Visualization (Optional) |
I edit, organize, and add more examples to the content of this review is such a way that it is more understandable. However, it is important to mention that this review is based on three main sources:
- The official pandas user guide
- A real python review: The pandas DataFrame: Make Working With Data Delightful
- A medium article: Pandas Illustrated: The Definitive Visual Guide to Pandas