Category: pandas
Data Analysis with Pandas (Guide)
Python Pandas is a powerhouse Data Analysis Library. This highperformance 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:
 Exploring Pandas Data Structures
 Understanding the Pandas Filter
 Harnessing the Power of Pandas groupby
 How to Read XLS Files with Pandas
 Integrating SQLite Databases with Pandas
 Visualizing Your Data Using Pandas
 Reading CSV Files Made Easy with Pandas
 Simplified Process to Read Excel with Pandas
 Efficiently Write Excel Data 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: Multidimensional 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.
Related course:
Data Analysis with Python and Pandas: Go from zero to hero
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) 
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 pandasexample 
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:

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:

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.
'Animal': ['Elephant','Cat','Cat','Horse','Horse','Cheetah', 'Cheetah'], 'Speed': [20,30,27,50,45,70,66]}) df = pd.DataFrame({ 
You can group by animal and the average speed.
'Animal']).mean() df.groupby([ 
If you have multiple columns in your table like so:
'Animal': ['Elephant','Cat','Cat','Horse','Horse','Cheetah', 'Cheetah'], df = pd.DataFrame({ 
Then you can add the column like this:
"Animal")["Speed"].mean() df.groupby( 
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/seaborndata/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 
You can retrieve the same with:
iris.head() 
You can easily retrieve the minimum and maximum of a column
'sepal_width'].min() iris[ 
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.
"species")["sepal_width"].mean() iris.groupby( 
If you want the minimum value for each sepal width and species, you’d use:
"species")["sepal_width"].min() iris.groupby( 
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:

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

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).

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

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

It creates the SQLite database containing one table with dummy data.
SQLite dataset created from scriptSqlite to Python Panda Dataframe
An SQL query result can directly be stored in a panda dataframe:

We connect to the SQLite database using the line:

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

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 highperformance, easytouse 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:

Imported in excel that will look like this:
Python Pandas example datasetThe data can be read using:

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:

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 
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: 
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'] 
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 
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.