**Read in a CSV dataset:** `pd.DataFrame.from_csv("csv_file")` OR `pd.read_csv("csv_file")`

**Read in an Excel dataset:** `pd.read_excel("excel_file")`

**Basic dataset feature info:** `df.info()`

**Basic dataset statistics:** `print(df.describe())`

**Print dataframe in a table:** `print(tabulate(print_table, headers=headers))` where "print_table" is a list of lists and "headers" is a list of the string headers

**Drop missing data:** `df.dropna(axis=0, how='any')` Return object with labels on given axis omitted where alternately any or all of the data are missing

**Replace missing data:** `df.replace(to_replace=None, value=None)` Replace values given in "to_replace" with "value".

**Check for NANs:** `pd.isnull(object)` Detect missing values (NaN in numeric arrays, None/NaN in object arrays)

**Drop a feature:** `df.drop('feature_variable_name', axis=1)` axis is either 0 for rows, 1 for columns

**Convert object type to float:** `pd.to_numeric(df["feature_name"], errors='coerce')` Convert object types to numeric to be able to perform compuations

**Convert DF to numpy array:** `df.as_matrix()`

**Get first "n" rows:** `df.head([n])`

**Get data by feature name:** `df.loc[feature_name]`

**Area plot:** `df.plot.area([x, y])`

**Vertical bar plot:** `df.plot.bar([x, y])`

**Horizontal bar plot:** `df.plot.barh([x, y])`

**Boxplot:** `df.plot.box([by])`

**Histogram:** `df.plot.hist([by, bins])`

**Line plot:** `df.plot.line([x, y])`

**Pie chart:** `df.plot.pie([y])`