Quick Introduction to Python Pandas

Pandas is Python Data Analysis Library. It was developed by Wes Mckinney.

Python Pandas is an open-source, fast, easy-to-use tool for data manipulation and data analysis. It provides rich data structures and functions to make data analysis easy and fast.

Install Pandas

It is very easy to install Pandas module with PIP tool -

pip install pandas

On successful installation, it returns something like this -

Installing collected packages: pandas
Successfully installed pandas-1.0.1

Pandas Data Structures

Pandas has two mostly used workhorse data structures - Series and Data Frames.

Series

A Series is one-dimensional data structure containing an array of data and an associative array of data labels, called its index.

pandas.Series( data, index, dtype, copy)

Here, the data can be ndarray, lists, constants, index is unique value, dtype is datatype, copy is a copy data, which is by default FALSE.

import pandas as pd
getData = pd.Series(['a','b','c','d','e'],
                    index=[1,2,3,4,5])
print(getData)

#retrieve the second element
print(getData[1])

#retrieve the first three elements
print(getData[:3])

1    a
2    b
3    c
4    d
5    e
dtype: object
a
1    a
2    b
3    c
dtype: object

Data Frame

A DataFrame is a tabular-like data structure containing an ordered collection of columns. The each column can be of different data types, like numeric, Boolean, strings, etc. It has both a row and a column index.

pandas.DataFrame( data, index, columns, dtype, copy)

Here, the data can be ndarray, series, dict, map, lists, constants and also dataframe. The index is used for the resulting frame, the columns are used for column labels, dtype is the data type of each column and the copy is used for copying data.

import pandas as pd

data = [['Biscuit',6],['Chips',3],['Candy',20]]
getData = pd.DataFrame(data,columns=['Item','Quantity'])
print(getData)

      Item  Quantity
0  Biscuit         6
1    Chips         3
2    Candy        20

Pandas Iteration

Python Pandas provides three methods for iterations - iteritems(), iterrows() and itertuples().

iteritems()

It iterates over the key, value pairs.

import pandas as pd

data = [['Biscuit',6],['Chips',3],['Candy',20]]
getData = pd.DataFrame(data,columns=['Item','Quantity'])

for key,value in getData.iteritems():
    print(key, value)

Item 0    Biscuit
1      Chips
2      Candy
Name: Item, dtype: object
Quantity 0     6
1     3
2    20
Name: Quantity, dtype: int64

iterrows()

It returns each index value along with the data containing in each row.

import pandas as pd

data = [['Biscuit',6],['Chips',3],['Candy',20]]
getData = pd.DataFrame(data,columns=['Item','Quantity'])

for key,value in getData.iterrows():
    print(key, value)

0 Item        Biscuit
Quantity          6
Name: 0, dtype: object
1 Item        Chips
Quantity        3
Name: 1, dtype: object
2 Item        Candy
Quantity       20
Name: 2, dtype: object

itertuples()

It returns an iterator yielding a named tuple for each row in the DataFrame.

import pandas as pd

data = [['Biscuit',6],['Chips',3],['Candy',20]]
getData = pd.DataFrame(data,columns=['Item','Quantity'])

for row in getData.itertuples():
    print(row)

Pandas(Index=0, Item='Biscuit', Quantity=6)
Pandas(Index=1, Item='Chips', Quantity=3)
Pandas(Index=2, Item='Candy', Quantity=20)

Pandas Sorting

These are the following sorting techniques used in Python Pandas -

sort_index()

The sort_index() method sort the series or data frames by index labels.

import pandas as pd

data = [['Chips',3],['Candy',20],['Biscuit',6],['Jelly',4]]
getData = pd.DataFrame(data,columns=['Item','Quantity'],index=[5,4,1,3])
print(getData)

sorted_df = getData.sort_index()
print(sorted_df)

      Item  Quantity
5    Chips         3
4    Candy        20
1  Biscuit         6
3    Jelly         4
      Item  Quantity
1  Biscuit         6
3    Jelly         4
4    Candy        20
5    Chips         3

If we want to sort the data in descending order, simply pass 'ascending=False' -

import pandas as pd

data = [['Chips',3],['Candy',20],['Biscuit',6],['Jelly',4]]
getData = pd.DataFrame(data,columns=['Item','Quantity'],index=[5,4,1,3])

sorted_df = getData.sort_index(ascending=False)
print(sorted_df)

      Item  Quantity
5    Chips         3
4    Candy        20
3    Jelly         4
1  Biscuit         6

sort_values

The sort_values() method sort the series or data frames by values. In the below example, we have sorted the by 'Quantity' values.

import pandas as pd

data = [['Chips',3],['Candy',20],['Biscuit',6],['Jelly',4]]
getData = pd.DataFrame(data,columns=['Item','Quantity'],index=[5,4,1,3])

sorted_df = getData.sort_values(by='Quantity')
print(sorted_df)

      Item  Quantity
5    Chips         3
3    Jelly         4
1  Biscuit         6
4    Candy        20

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