Cloud Academy – Working With Pandas-STM
English | Size: 540.80 MB
Category: Tutorial
What is Pandas
Pandas is Python’s ETL package for structured data
Built on top of numpy, designed to mimic the functionality of R dataframes
Provides a convenient way to handle tabular data
Can perform all SQL functionalities, including group-by and join.
Compatible with many other Data Science packages, including visualisation packages such as Matplotlib and Seaborn
Defines two main data types:
pandas.Series
pandas.DataFrame
Series
Generalised array — can be viewed as a table with a single column
It consists of two numpy arrays:
Index array: stores the index of the elements
values array: stores the values of the elements
Each array element has an unique index (ID), contained in a separate index array
If we reorder the series, the index moves with element. So an index will always identify with the same element in the series
Indices do not have to be sequential, they do not even have to be numbers.
Think indices as the primary keys for each row in a single column table
DataFrames
A pandas DataFrame represents a table, it contains
Data in form of rows and columns
Row IDs (the index array, i.e. primary key)
Column names (ID of the columns)
A DataFrame is equivalent to collection of Series with each Series representing a column
The row indices by default start from 0 and increase by one for each subsequent row, but just like Series they can be changed to any collection of objects
Each row index uniquely identifies a particular row. If we reorder the rows, their indices go with them
Group By
Groups are usually used together with reductions
Counting number of rows in each group
my_dataframe.groupby(criteria).size()
Sum of every numerical column in each group
my_dataframe.groupby(criteria).sum()
Mean of every numerical column in each group
my_dataframe.groupby(criteria).mean()
Join
Use DataFrame.merge() as a general method of joining two dataframes:
Works also with series
Joins on the primary keys of the two dataframes (series)
Missing Values
Finding out number of missing values in each column
my_dataframe.isna().sum()
Removing rows
my_dataframe.dropna(axis = 0)
Removing columns
my_dataframe.dropna(axis = 1)
Filling with a value
For all missing values: my_dataframe.fillna(replacement_value)
Different value for each column: my_dataframe.fillna({‘NAME’: ‘UNKNOWN’, ‘AGE’: 0})
Map, Replace, Apply
Map applies a mapping to every element of the dataframe
my_dataframe.map({old1: new1, old2: new2, .})
my_dataframe.map(function)
If we provide map using a dictionary, then any elements not in the keys will be mapped to numpy.nan
Replace applies a mapping to only elements of the dataframe that have been mentioned in the mapping
my_dataframe.replace ({old1: new1, old2: new2, .})
Any elements not in the dictionary keys will not be changed
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