Category: pandas

Data Analysis with Pandas (Guide)

Python Pandas is a powerhouse Data Analysis Library. This high-performance tool offers data structures tailored for simplifying structured data and time series analysis. With Pandas, data manipulation becomes a breeze. Here’s a quick glance at what Pandas brings to the table:

DataFrame object: Streamlines data manipulation processes.
Read/Write capability: Seamlessly interact with data from sources like Microsoft Excel, CSV, SQL databases, and HDF5.
Advanced features: Includes reshaping, slicing, indexing and many other functionalities for comprehensive data management.

Mastering Pandas: To delve deeper into the world of Pandas and elevate your data analysis skills, consider this Data Analysis with Python Pandas course.

Deep Dive Articles: Dive into these articles that detail various functionalities and aspects of Data Analysis with Pandas:

Core Data Structures in Data Analysis: Understanding the common data structures can be pivotal for efficient data analysis. Here’s a breakdown:

Tabular data: Comparable to SQL tables or Excel spreadsheets.
Time series data: Chronological data points, ideal for trend analysis.
Matrix data: Multi-dimensional data, perfect for complex calculations.
Statistical data sets: Data collated for analysis, often in the realms of research or business intelligence.

Ready for more? Dive into the next segment on

Pandas Data Structures

pandas filter

pandas groupby

DataFrames data can be summarized using the groupby() method. In this article we’ll give you an example of how to use the groupby method.

This tutorial assumes you have some basic experience with Python pandas, including data frames, series and so on. If you are new to Pandas, I recommend taking the course below.

What does groupby do?

The idea of groupby() is pretty simple: create groups of categories and apply a function to them. Groupby has a process of splitting, applying and combining data.

splitting: the data is split into groups
applying: a function is applied to each group
combining: the results (data of different datasets is combined after applying groupby, resulting in a new data structure)

If you programmed databases (SQL) before, you may be familiar with a query like this:

SELECT city, count(name)
FROM df
GROUP BY city
ORDER BY city;

Pandas groupby does a similar thing. But then you’d type

print(df1.groupby(["City"])[['Name']].count())

Installing pandas

If you don’t have the pandas data analysis module installed, you can run the commands:

$ python -m venv pandas-example
$ source ./pandas-example/bin/activate
$ python -m pip install pandas

This sets up a virtual environment and install the pandas module inside it.

Pandas groupby example

Start by importing pandas, numpy and creating a data frame.

import pandas as pd

Our data frame contains simple tabular data:

In code the same table is:


import pandas as pd
import numpy as np

df1 = pd.DataFrame( { 
    "Name" : ["Alice", "Ada", "Mallory", "Mallory", "Billy" , "Mallory"] , 
    "City" : ["Sydney", "Sydney", "Paris", "Sydney", "Sydney", "Paris"]} )

You can then summarize the data using the groupby method. In our example there are two columns: Name and City.

The function .groupby() takes a column as parameter, the column you want to group on.
Then define the column(s) on which you want to do the aggregation.

print df1.groupby(["City"])[['Name']].count()

This will count the frequency of each city and return a new data frame:

The total code being:


import pandas as pd
import numpy as np

df1 = pd.DataFrame( { 
    "Name" : ["Alice", "Ada", "Mallory", "Mallory", "Billy" , "Mallory"] , 
    "City" : ["Sydney", "Sydney", "Paris", "Sydney", "Sydney", "Paris"]} )


df2 = df1.groupby(["City"])[['Name']].count()
print(df2)

Example 2

The groupby() operation can be applied to any pandas data frame.
Lets do some quick examples.

The data frame below defines a list of animals and their speed measurements.

>>> df = pd.DataFrame({'Animal': ['Elephant','Cat','Cat','Horse','Horse','Cheetah', 'Cheetah'], 'Speed': [20,30,27,50,45,70,66]})
>>> df
     Animal      Speed
0  Elephant         20
1       Cat         30
2     Horse         50
3   Cheetah         70
>>>

You can group by animal and the average speed.

>>> df.groupby(['Animal']).mean()
          Speed
Animal         
Cat        28.5
Cheetah    68.0
Elephant   20.0
Horse      47.5
>>>

If you have multiple columns in your table like so:

>>> df = pd.DataFrame({'Animal': ['Elephant','Cat','Cat','Horse','Horse','Cheetah', 'Cheetah'], 
... 'Speed': [20,30,26,50,45,70,60],
... 'Length': [8,0.5,0.6,2,2.1,1.8,1.7]})

Then you can add the column like this:

>>> df.groupby("Animal")["Speed"].mean()
>>> df.groupby("Animal")["Length"].mean()

Example 3: Real World Data

The Iris flower data set contains data on several flower species and their measurements. You can load it the whole data set from a csv file like this:

iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')

You can read any csv file with the .read_csv() function like this, directly from the web.

To give you some insight into the dataset data:

   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa

You can retrieve the same with:

>>> iris.head()

You can easily retrieve the minimum and maximum of a column

>>> iris['sepal_width'].min()
2.0
>>> iris['sepal_width'].max()
4.4
>>>

You’ve seen the basic groupby before. You can apply groupby while finding the average sepal width.

This then returns the average sepal width for each species.

>>> iris.groupby("species")["sepal_width"].mean()
species
setosa        3.428
versicolor    2.770
virginica     2.974
Name: sepal_width, dtype: float64
>>>

If you want the minimum value for each sepal width and species, you’d use:

>>> iris.groupby("species")["sepal_width"].min()
species
setosa        2.3
versicolor    2.0
virginica     2.2
Name: sepal_width, dtype: float64
>>>

Conclusion

We’ve covered the groupby() function extensively. You can now apply the function to any data frame, regardless of wheter its a toy dataset or a real world dataset.

You’ve learned:

how to load a real world data set in Pandas (from the web)
how to apply the groupby function to that real world data.

If you are interested in learning more about Pandas, check out this course:
Data Analysis with Python and Pandas: Go from zero to hero

pandas read xls

You can use pandas to read data from an Excel file into a DataFrame, and then work with the data just like you would any other dataset.

To read an Excel file into a DataFrame using pandas, you can use the read_excel() function. The read_excel() function returns a DataFrame by default, so you can access the data in your DataFrame using standard indexing and slicing operations.

Pandas, a data analysis library, has native support for loading excel data (xls and xlsx). The method read_excel loads xls data into a Pandas dataframe:


read_excel(filename)

If you have a large excel file you may want to specify the sheet:


df = pd.read_excel(file, sheetname='Elected presidents')

Related course
Data Analysis with Python Pandas

Read excel with Pandas
The code below reads excel data into a Python dataset (the dataset can be saved below).


from pandas import DataFrame, read_csv
import matplotlib.pyplot as plt
import pandas as pd 

file = r'data/Presidents.xls'
df = pd.read_excel(file)
print(df['Occupation'])

The dataframe can be used, as shown in the example below:


from pandas import DataFrame, read_csv
import matplotlib.pyplot as plt
import pandas as pd 

file = r'data/Presidents.xls'
df = pd.read_excel(file)

# remove messy data
df = df[df['Years in office'] != 'n/a']

# show data
print('Min: ', df['Years in office'].min())
print('Max: ', df['Years in office'].max())
print('Sum: ', df['Years in office'].sum())

Dataset
For purpose of demonstration, you can use the dataset from: depaul.edu.

A large dataset stored in XLS format

pandas sqlite

An SQLite database can be read directly into Python Pandas (a data analysis library). In this article we’ll demonstrate loading data from an SQLite database table into a Python Pandas Data Frame. We’ll also briefly cover the creation of the sqlite database table using Python.

Related course
Data Analysis with Python Pandas

SQLite dataset

We create a simple dataset using this code:


import sqlite3 as lite
import sys
 
con = lite.connect('population.db')
 
with con:
    cur = con.cursor()    
    cur.execute("CREATE TABLE Population(id INTEGER PRIMARY KEY, country TEXT, population INT)")
    cur.execute("INSERT INTO Population VALUES(NULL,'Germany',81197537)")
    cur.execute("INSERT INTO Population VALUES(NULL,'France', 66415161)")
    cur.execute("INSERT INTO Population VALUES(NULL,'Spain', 46439864)")
    cur.execute("INSERT INTO Population VALUES(NULL,'Italy', 60795612)")
    cur.execute("INSERT INTO Population VALUES(NULL,'Spain', 46439864)")

It creates the SQLite database containing one table with dummy data.

SQLite dataset created from script

Sqlite to Python Panda Dataframe

An SQL query result can directly be stored in a panda dataframe:


import pandas as pd
import sqlite3

conn = sqlite3.connect('population.db')
query = "SELECT country FROM Population WHERE population > 50000000;"

df = pd.read_sql_query(query,conn)

for country in df['country']:
    print(country)

We connect to the SQLite database using the line:


conn = sqlite3.connect('population.db')

The line that converts SQLite data to a Panda data frame is:


df = pd.read_sql_query(query,conn)

where query is a traditional SQL query.

The dataframe (df) will contain the actual data.

visualize data with python

pandas read csv

Pandas is a data analaysis module. It provides you with high-performance, easy-to-use data structures and data analysis tools.

In this article you will learn how to read a csv file with Pandas.

Related course
Data Analysis with Python Pandas

Read CSV with Python Pandas
We create a comma seperated value (csv) file:


Names,Highscore,
Mel,8,
Jack,5,
David,3,
Peter,6,
Maria,5,
Ryan,9,

Imported in excel that will look like this:

Python Pandas example dataset

The data can be read using:


from pandas import DataFrame, read_csv
import matplotlib.pyplot as plt
import pandas as pd 

file = r'highscore.csv'
df = pd.read_csv(file)
print(df)

The first lines import the Pandas module. The read_csv method loads the data in a a Pandas dataframe that we named df.

Dataframes
A dataframe can be manipulated using methods, the minimum and maximum can easily be extracted:


from pandas import DataFrame, read_csv
import matplotlib.pyplot as plt
import pandas as pd 

file = r'highscore.csv'
df = pd.read_csv(file)
print('Max', df['Highscore'].max())
print('Min', df['Highscore'].min())

Pandas on a dataset

The dataset in this example is very small, but a dataset can easily contain thousands or millions of records.

Read Excel with Pandas

Leveraging Pandas for Excel File Operations in Python
Excel files have always been a popular choice for data storage and analysis. With Python’s Pandas library, handling Excel files has never been easier. Dive in to learn how to efficiently read Excel files using Pandas.

Effortlessly Read Excel with Pandas
Pandas provides a rich set of methods to read, analyze, and manipulate Excel files with ease. By the end of this tutorial, you will be proficient in reading Excel columns, fetching specific data, and iterating through Excel data.

Accessing Excel Column Names with Pandas
To fetch column names from an Excel file, first, we need to load the data into a Pandas DataFrame. The method read_excel() is specifically designed for this purpose. Here’s how you can do it:

import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile

df = pd.read_excel('File.xlsx', sheet_name='Sheet1')

print("Column headings:")
print(df.columns)

Once the data is loaded into a DataFrame, accessing column names is as easy as calling df.columns.

Fetching Data from a Specific Excel Column
Once you have the data in a Pandas DataFrame, fetching data from a specific column becomes trivial. Here’s how you can fetch all data from the ‘Sepal width’ column:

print(df['Sepal width'])

Iterating Through Excel Data with Pandas
Sometimes, you might want to loop through the data. Here’s how you can iterate over the ‘Sepal width’ column:

for i in df.index:
    print(df['Sepal width'][i])

Storing Excel Data in Python Lists
If you prefer working with lists, Pandas allows you to easily convert a column’s data to a list:

listSepalWidth = df['Sepal width']
print(listSepalWidth[0])

Extracting Multiple Columns from an Excel Sheet
You’re not limited to working with one column at a time. Here’s how you can fetch multiple columns:

import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile

df = pd.read_excel('File.xlsx', sheet_name='Sheet1')

sepalWidth = df['Sepal width']
sepalLength = df['Sepal length']
petalLength = df['Petal length']

Boost Your Python Data Analysis Skills
For those looking to further enhance their Python data analysis skills using Pandas, consider checking out the Data Analysis with Python Pandas course.