Glossary

Below is the summary of all the functions and methods that you learned in this lesson:

Category: Initialization and Utility

Function/Method	Description
`pandas.read_csv(relative_path_to_file)`	Reads a comma-separated values (csv) file present at `relative_path_to_file` and loads it as a DataFrame
`pandas.DataFrame(data)`	Returns a 2-D heterogeneous tabular data. Note: There are other optional arguments as well that you can use to create a dataframe.
`pandas.Series(data, index)`	Returns 1-D ndarray with axis labels
`pandas.Series.shape` `pandas.DataFrame.shape`	Returns a tuple representing the dimensions
`pandas.Series.ndim` `pandas.DataFrame.ndim`	Returns the number of the dimensions (rank). It will return 1 in case of a Series
`pandas.Series.size` `pandas.DataFrame.size`	Returns the number of elements
`pandas.Series.values`	Returns the data available in the Series
`pandas.Series.index`	Returns the indexes available in the Series
`pandas.DataFrame.isnull()`	Returns a same sized object having True for NaN elements and False otherwise.
`pandas.DataFrame.count(axis)`	Returns the count of non-NaN values along the given axis. If axis=0, it will count down the dataframe, meaning column-wise count of non-NaN values.
`pandas.DataFrame.head([n])`	Return the first n rows from the dataframe. By default, n=5.
`pandas.DataFrame.tail([n])`	Return the last n rows from the dataframe. By default, n=5. Supports negative indexing as well.
`pandas.DataFrame.describe()`	Generate the descriptive statistics, such as, count, mean, std deviation, min, and max.
`pandas.DataFrame.min()`	Returns the minimum of the values along the given axis.
`pandas.DataFrame.max()`	Returns the maximum of the values along the given axis.
`pandas.DataFrame. mean()`	Returns the mean of the values along the given axis.
`pandas.DataFrame.corr()`	Compute pairwise correlation of columns, excluding NA/null values.
`pandas.DataFrame.rolling(windows)`	Provide rolling window calculation, such as `pandas.DataFrame.rolling(15).mean()` for rolling mean over window size of 15.
`pandas.DataFrame.loc[label]`	Access a group of rows and columns by label(s)
`pandas.DataFrame.groupby(mapping_function)`	Groups the dataframe using a given mapper function or or by a Series of columns.

Category: Manipulation

Function/Method	Description
`pandas.Series.drop(index)`	Drops the element positioned at the given index(es)
`pandas.DataFrame.drop(labels)`	Drop specified labels (entire columns or rows) from the dataframe.
`pandas.DataFrame.pop(item)`	Return the item and drop it from the frame. If not found, then raise a KeyError.
`pandas.DataFrame.insert(location, column, values)`	Insert column having given values into DataFrame at specified location.
`pandas.DataFrame.rename(dictionary-like)`	Rename label(s) (columns or row-indexes) as mentioned in the `dictionary-like`
`pandas.DataFrame.set_index(keys)`	Set the DataFrame's row-indexes using existing column-values.
`pandas.DataFrame.dropna(axis)`	Remove rows (if axis=0) or columns (if axis=1) that contain missing values.
`pandas.DataFrame.fillna(value, method, axis)`	Replace NaN values with the specified value along the given axis, and using the given method (‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None)
`pandas.DataFrame.interpolate(method, axis)`	Replace the NaN values with the estimated value calculated using the given method along the given axis.

For any other requirement, refer to the complete list of function definitions for DataFrame and Series classes.

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