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Python is a popular programming language for data analytics because it is easy to learn, flexible, and has a variety of libraries for data manipulation and visualization. It is also a general-purpose language, so it can add more functionality to data analytics software than domain specific languages. Python is one of the easiest coding languages to learn. It helps data analysts to make sense of complicated data sets and make it easier to understand.
Libraries such as "Pandas" and "NumPy" provide tools for data manipulation, while "Matplotlib" and "Seaborn" are essential for data visualization.
This blog will explore one of the most popular python libraries “Pandas”.
What is “Pandas”?
Pandas is a python package user for working with data sets. It has functions for analyzing, cleaning, and manipulating data. The name ‘Pandas’ has a reference to both ‘Panel data’ and ‘Python Data Analysis’. Pandas library is used in almost any process of extracting information from data using code. Python library can easily manipulate data and conduct data analysis operations. Pandas are an essential component of data science life cycle. Along with NumPy in Matplotlib, it is the most popular and widely used python library for data science.
Why “Pandas”?
Pandas can clean up messy data sets and organize multiple variables by creating powerful visualizations. Pandas library accelerates workflow when dealing with large data sets compared to other python libraries. This library can perform data manipulation operations on data with just a few lines of code. Users can simplify data visualization using this library, even with unstructured data like images, videos, etc., For structured data like data structures and databases, Pandas provide powerful tools for filtering, grouping, and joining data.
Installing Pandas:
We can use "PIP" to install libraries in Python. So, to install “Pandas” , we will run below command.
pip install pandas
Now, the “Pandas” are ready to be used in python program using “import” option.
import pandas ad pd
How Pandas Works:
Here is a closer look at how pandas works and the mechanisms behind its efficiency. Let’s start with understanding the data structure first.
Data Structure:
Series: A Pandas Series is a one-dimensional array capable of holding any type (Integer, String, Floating point numbers, etc.,). It Is labeled, meaning each element has an index like a row label in spread sheet. Pandas series can be created from lists, dictionaries, etc.,
e.g,.
import pandas as pd
s = pd.Series([2,4,8], index=[‘a’,’b’,’c’])
Data Frame: Data Frame is a two-dimensional data structure with labeled axes, resembling a table with rows and columns. Each column in the Data frame can be a different data type(Integers, String, Floating point numbers etc.)
e.g.,
data1= { ‘name’ : [ ‘jack’, ’rose’, ’paul’ ],
‘age’ : [34,32,22],
‘city’ : [‘CA’,’NY’,’NJ’]
}
df= pd.DataFrame(data)
Here’s some of key operations that can be performed by using pandas.
Data selection: Pandas allow us to select data using labels, indices, and Boolean conditions.
# To select a column
df['name']
The output is as follow.
name
0 jack
1 rose
2 paul
# To select multiple columns
df[['name', 'age']]
The output is as follows.
name age
0 jack 34
1 rose 32
2 paul 22
# To select rows based on condition
df[df['age'] > 30]
The output is as follows.
name age
0 jack 34
1 rose 32
Data Manipulation: Pandas provides functions for adding, deleting, and modifying columns and rows.
# Add a new column
df['salary'] = [70000, 80000, 90000]
The output is as follows.
name age city salary
0 jack 34 CA 70000
1 rose 32 NY 80000
2 paul 22 NJ 90000
# Apply a function to a column
df['age'] = df['age'].apply(lambda x: x + 1)
The output is as follows.
name age city salary
0 jack 35 CA 70000
1 rose 33 NY 80000
2 paul 23 NJ 90000
Handling missing data: Pandas has methods for detecting and handling missing data.
# To check for missing values
df.isnull()
Assuming 'df' has some missing values.
# df.loc[1, 'salary'] = None
print(df.isnull())
The output is as follows.
name age city salary
0 False False False False
1 False False False True
2 False False False False
# To fill missing values
df.fillna(0)
The output is as follows.
name age city salary
0 jack 35 CA 70000.0
1 rose 33 NY 0.0
2 paul 23 NJ 90000.0
# To drop rows with missing values
df.dropna()
The output is as follows.
name age city salary
0 jack 35 CA 70000
2 paul 23 NJ 90000
Merging And Joining: Combine data from different Data Frames using joins.
df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]})
df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]})
merged_df = pd.merge(df1, df2, on='key')
The output is as follows.
key value_x value_y
0 A 1 4
1 B 2 5
Grouping and Aggregation: Grouping data based on a specific column and performing aggregate functions is straightforward.
# To Group by 'City' and calculate mean age
avg_age_city=df.groupby('city')['age'].mean()
The output is as follows.
city age
CA 35
NY 33
NJ 23
Key Features:
Pandas library was created to be able to work with large data sets faster than any other library. It excels at analyzing huge amounts of data.
Pandas data structure is efficient. And this can be easily integrated with many other libraries.
Data merging is simple in various situations. User can merge small, medium, or large datasets with pandas.
It supports automatic and explicit data alignment. And easy handling of missing data, dataset merging, joining.
Conclusion:
From learning data structure and data manipulation to powerful data analysis algorithms, pandas library is the best for leveraging the power of python in data analysis. With its object-oriented approach, pandas simplifies and speeds up discovering insights from our data. Pandas library is easy to learn and use for both beginners and advanced users. Whether working with spreadsheets, text, databases, videos, pandas can help us to gain valuable insights from our data and make informed decisions.
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