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Pandas in Python refer to an open-source library that delivers high-performance, easy-to-use data structures and different data analysis tools. Python Panda is employed in a wide range of fields, including finance, academics, statistics, and more. It is well-suited for different types of data, including statistical data sets, unlabeled data, ordered and unordered time series data, and more. Dive into this article to learn more about Pandas in Python.
Table of Content:
Importance of Panda in Data Analysis
Python Panda is a core library for data analysis. It is a base package with additional functionality from various other packages. Pandas in Python provide the ability to organize structured data into an array so that it can be managed easily.
Python Panda is useful for performing the following tasks:
- Data wrangling
- Reading and writing
- Simple plotting
- Logical processes
- Updating data
- SQL join
- Instance counting
Therefore, the importance of Python Panda in data analysis stems from its potential to make data sets more accessible and comprehensible.
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Key Features of Python Pandas
A few key features of Pandas in Python are as follows:
- Comes with a fast and efficient DataFrame object using default and customized indexing
- Useful for reshaping data sets
- Valuable for the alignment and integration of missing data
- Grouping data for transformations and aggregations
- Delivers the functionality of the time series
- Can process data in different formats like time series, tabular heterogenous, matrix data, and more
- Ability to handle different operations like slicing, subsetting, filtering, and re-shaping data sets
- Supports integration with different libraries like SciPy
- Deliver fast performance
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Primary Data Structures in Pandas: Series and DataFrame
The two primary data structures in Python Panda include series and DataFrame
Python Pandas Series
It is a one-dimensional array that contains different types of data. The row labels in a series are referred to as the index. A series cannot have multiple columns and comes with only one parameter.
Creating a Series in Panda Python
To create a series, you will have to import the numpy module and use the array() function. The code is as follows:
import pandas as pd
import numpy as np
info = np.array(['P','a','n','d','a','s'])
a = pd.Series(info)
print(a)
Output:
0 P
1 a
2 n
3 d
4 a
5 s
dtype: object
Python Pandas DataFrame
It is suitable for a two-dimensional array with labeled rows and columns. Python Pandas DataFrame is widely used and comes with the row index and column index. The Pandas DataFrame in Python comes with the following features:
The columns can be heterogeneous with int, bool, and others
It can be defined as a dictionary of Series structures with indexed rows and columns.
Creating a DataFrame
You can easily make a DataFrame in Python with the help of a list. The code for creating a DataFrame is as follows:
import pandas as pd
# a list of strings
x = ['Python', 'Pandas']
# Calling DataFrame constructor on list
df = pd.DataFrame(x)
print(df)
Output:
0
0 Python
1 Pandas
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Python Pandas Sorting
You will come across two types of sorting techniques in Python Panda. They are as follows:
By Label
The DataFrame can be sorted with the help of the sort_index() method. The code is as follows:
import pandas as pd
import numpy as np
unsorted_df = pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
mns = ['col2','col1'])
sorted_df=unsorted_df.sort_index()
print sorted_df
The output is as follows:
col2 col1
0 0.208464 0.627037
1 0.641004 0.331352
2 -0.038067 -0.464730
3 -0.638456 -0.021466
4 0.014646 -0.737438
5 -0.290761 -1.669827
6 -0.797303 -0.018737
7 0.525753 1.628921
8 -0.567031 0.775951
9 0.060724 -0.322425
By default, sorting on row labels takes place in ascending order.
By Actual Value
You can use the sort_values() method to perform sorting according to values. The code is as follows:
import pandas as pd
import numpy as np
unsorted_df = pd.DataFrame({'col1':[2,1,1,1],'col2':[1,3,2,4]})
sorted_df = unsorted_df.sort_values(by='col1')
print sorted_df
The output is as follows:
col1 col2
1 1 3
2 1 2
3 1 4
0 2 1
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Python Pandas GroupBy
The groupby function in Python Panda can perform one of the following functions on original data:
- Splitting the object
- Combining the result
- Applying a function
The syntax for dataframe.groupby() is as follows:
DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)Python Pandas: Merging
If you are using Pandas in Python, you will be able to merge two DataFrames in the following way:
import pandas as pd
data1 = {
"name": ["Sally", "Mary", "John"],
"age": [50, 40, 30]
}
data2 = {
"name": ["Sally", "Peter", "Micky"],
"age": [77, 44, 22]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
newdf = df1.merge(df2, how='right')
2: Tips and Tricks for Efficient Pandas Usage
A few tips and tricks for using Pandas in Python are as follows:
Configure Settings and Options at Interpreter Startup
It is a major productivity saver that sets customized pandas options at interpreter startup, particularly when you are working in a scripting environment. For the purpose of Pandas in Python configuration, you will be able to use the pd.set_option().H3:Create Toy Data Structures Using the Testing Module
Pandas in Python comes with a testing module that offers various convenient functions. Pandas in Python lets you use these toy data structures for the purpose of testing assertions, benchmarking, experimenting, and more.
Conclusion
Built on the Numpy library, Python Panda is valuable for data analysis, machine learning, and more. It comes with two types of data structures, including the series and DataFrames. Moreover, Pandas in Python can be used with a wide variety of libraries applicable to data science.
FAQs
What are Pandas used for in Python?
Pandas in Python offer fast, expressive, and flexible data structures to make working with labeled or relational data easy and intuitive. It is usually the basic high-level building block for performing real-world, practical data analysis with the help of Python.
When should you start using Python Pandas?
You need to start using Pandas in Python when you have to perform different data analysis tasks. Pandas in Python can also be used for various machine learning tasks. Python is built on top of another package called Numpy that offers support for multi-dimensional arrays.
How do I handle missing data in a DataFrame using Python Pandas?
The steps for handling missing data in a DataFrame using Python Pandas are as follows:
- Import the required packages.
- Use the red-CSV () function for going through the dataset.
- The dataset gets printed, and you need to check when the record has missing data or NaN functions.
- Next, the dropna() function is applied to the dataset.
- The dataset gets printed.
What are some common techniques for cleaning and preprocessing data with Pandas?
A few common techniques for cleaning and preprocessing data with Pandas in Python are as follows:
- Changing the DataFrame index
- Dropping the DataFrame columns
- Combining NumPy and str Methods to clean columns
Updated on May 1, 2024
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