Pandas Functions:
- pd.read_csv()
- Read a comma-separated value file (.csv) into Python as a DataFrame.
- pd.melt()
- Spread a column so that values stored in a single column can be made into columns as well.
- pd.pivot_table()
- Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame
- pd.concat()
- Concatenate pandas objects along a particular axis.
- pd.merge()
- Merge DataFrame objects by performing a column-column join similar to database-style join commands.
- pd.notnull()
- Check a Pandas object for missing values.
Regex Functions:
- re.compile()
- Compile a regular expression pattern into a Python object.
- re.findall()
- Return all non-overlapping matches of a pattern in a string, as a list of strings.
Commonly used Python Methods:
- .head()
- Return the first n rows in an object. The n defaults to 5.
- .tail()
- Return the last n rows in an object. As with .head(), the n defaults to 5.
- .info()
- Return information about a data frame, including the index and column data types, non-null values, and memory usage.
- .value_counts()
- Return an object containing counts of unique values for chosen data.
- .describe()
- Provides summary statistical information about chosen data.
- .split()
- Split each string in the chosen values based on a pattern.
- .astype()
- Coerce a Pandas object to a specific data type.
- .apply()
- Apply a function to each row or column in a data frame.
- .replace()
- Replace values passed to to_replace argument with specified values.
- .drop_duplicates()
- Return a data frame where duplicate rows have been removed from specified columns.
- .fillna()
- Fill in NA / NaN values using a specified method.
