Cheatsheet

Read and evaluate

pd.read_csv(): Reads a CSV file into a DataFrame.

df.head(): Returns the first n rows of the DataFrame.

df.tail(): Returns the last n rows of the DataFrame.

df.columns: Returns the column labels of the DataFrame.

df.shape: Returns a tuple (rows, columns) of the DataFrame.

df.empty: Indicates if the DataFrame is empty.

df['title'].unique(): Returns unique values of Series.

df['title'].value_counts(): Returns Series containing counts of unique value in column (example ’title’).

Access rows, columns, and cells

df['title']or df.title: Select single column with specific name (example ‘title’).

df.loc[]: Access rows & columns by label(s) or a boolean array.

df.iloc[]: Purely integer-location based indexing for selection by position.

df.iat[1, 2]: Access single value by index.

df.at[4, 'A']: Access single value by label.

Clean up

pd.isna(): Detects missing values.

pd.notna(): Detects non-missing values.

df.dropna(): Removes missing values from the DataFrame.

df.duplicated(): Returns boolean Series of duplicate rows.

df.drop_duplicates(): Removes duplicate rows from DataFrame.

Series.apply(): Invoke function on values of Series.

Series.str.rstrip(): Removes trailing characters.

Series.str.zfill(): Pads Series with zeros.

Series.str.strip(): Strips whitespaces from Series.

Loop through rows

df.iterrows(): Loops through DataFrame rows as (index, Series) pairs.

Merge DataFrames

pd.merge() : Merge DataFrame or named Series objects with a database-style join.

how=“left” : Merges on all ids from left DataFrame. Ids not in left DataFrame will not be included.

how=“right” : Merges on all ids from right DataFrame. Ids not in right DataFrame will not be included.

how=”outer” : Merges on all ids from both DataFrames.

how=“inner” : Merges only on ids found in both DataFrames. Ids found in only one DataFrame will not be included.

Reshaping

df.explode(): Transforms each element of a list-like to a row, replicating index values.

df.pivot(): Reshape data (produce a “pivot” table) based on column values.

df.pivot_table(): Create a spreadsheet-style pivot table as a DataFrame.

lambda: An anonymous (unnamed) function that applies arguments to various parameters and returns an expression (outcome).

df.melt(): Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

Sort DataFrame

df.sort_values(): Sort by the values along either axis.

Create new CSV

df.to_csv(): Writes the DataFrame to a CSV file