Pandas is a easy to use data analysis and manipulation tool. Pandas provides functionality for categorical,ordinal, and time series data . Panda provides fast and powerful calculations for data analysis.
Introduction to Pandas: A Guide
Dive Into Pandas: A Beginner’s Guide to Data Analysis in Python
Welcome to the fascinating world of data analysis in Python! Pandas is a powerhouse tool that simplifies the complexities of data manipulation and analysis. Whether you’re new to data science or brushing up on your skills, this guide will walk you through the essentials of Pandas, enriched with code examples to help you master the basics and more.
1. Installing Pandas Library
Before we dive into the data, let’s ensure you have Pandas installed. Open your terminal or command prompt and type:
pip install pandasThis command fetches and installs the Pandas library, setting the stage for your data manipulation journey.
2. Create A Series with the Help of the NumPy Array
Pandas Series are one-dimensional arrays capable of holding any data type. Let’s create a Series using a NumPy array:
Python
Python
Python
import pandas as pd
import numpy as np
data = np.array(['a', 'b', 'c', 'd'])
series = pd.Series(data)
print(series)3. Series with Mixed Datatype NumPy Array
Pandas gracefully handles multiple datatypes within a Series. Here’s how:
Python
Python
Python
data = np.array([1, "two", 3.0])
mixed_series = pd.Series(data)
print(mixed_series)4. Creating DataFrames from Lists with List Comprehension
DataFrames are two-dimensional data structures with labeled axes. Let’s explore different ways to create DataFrames:
Create a Multi-column DataFrame with List Comprehension
Python
Python
Python
df = pd.DataFrame([[i, i**2, i**3] for i in range(1, 6)], columns=['Number', 'Square', 'Cube'])
print(df)Create a Numbers Table with List Comprehension
Python
Python
Python
numbers_table = pd.DataFrame([[i * j for j in range(1, 6)] for i in range(1, 6)], columns=[f'x{i}' for i in range(1, 6)])
print(numbers_table)Create a Normalized Random Number DataFrame
Python
Python
Python
normalized_df = pd.DataFrame(np.random.randn(5, 5), columns=[f'Column{i}' for i in range(1, 6)])
print(normalized_df)5. Creating a DataFrame from Dictionary
Dictionaries offer a convenient way to create DataFrames:
Python
Python
Python
data_dict = {'Name': ['Anna', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'Occupation': ['Engineer', 'Doctor', 'Artist']}
df_from_dict = pd.DataFrame(data_dict)
print(df_from_dict)6. Creating a DataFrame from a NumPy Array with Random Integers
Python
Python
Python
random_int_df = pd.DataFrame(np.random.randint(0, 100, size=(5, 4)), columns=['A', 'B', 'C', 'D'])
print(random_int_df)7. Accessing External Data and Basic Statistical Analysis
Pandas simplifies data import from various sources. Here’s how to load a CSV file and perform basic analysis:
Python
Python
Python
df = pd.read_csv('your_file.csv')
print(df.columns) # Access column names
print(df.describe()) # Perform basic statistical analysisFinding Discrepancies in Data
Python
Python
Python
print(df.isnull().sum()) # Find NULL data points8. Data Access Techniques
Access Columns and Rows
Python
Python
Python
# Access columns
print(df['ColumnName'])
# Access rows by matching values in columns
print(df[df['ColumnName'] == 'Value'])
# Filter a dataset via a query
filtered_data = df.query('ColumnName > 20')9. Advanced Indexing and Handling NULL Data
Custom Indexes
Python
Python
Python
df.set_index('ColumnName', inplace=True)Handling NULL Data
Python
Python
Python
# Handle NULL data
df.fillna(0, inplace=True) # Replace NULL with 0Blank Data Treatment
Python
Python
Python
df.dropna(inplace=True) # Remove rows with NULL valuesEmbarking on your data analysis journey with Pandas opens up a world of possibilities. These foundational concepts and code examples are just the beginning. As you become more comfortable with Pandas, you’ll discover its power to transform and analyze data efficiently, setting you on a path to uncovering insights that drive impactful decisions. Happy coding!
ANCOVA: Analysis of Covariance with python
ANCOVA is an extension of ANOVA (Analysis of Variance) that combines blocks of regression analysis and ANOVA. Which makes it Analysis of Covariance.
Learn Python The Fun Way
What if we learn topics in a desirable way!! What if we learn to write Python codes from gamers data !!
Meet the most efficient and intelligent AI assistant : NotebookLM
Start using NotebookLM today and embark on a smarter, more efficient learning journey!
Break the ice
This can be a super guide for you to start and excel in your data science career.
Tourism Trend Prediction
After tourism was established as a motivator of local economies (country, state), many governments stepped up to the plate.
Sentiment Analysis Polarity Detection using pos tag
Sentiment analysis can determine the polarity of sentiments from given sentences. We can classify them into certain categories.
For loop with Dictionary
Traverse a dictionary with for loop Accessing keys and values in dictionary. Use Dict.values() and Dict.keys() to generate keys and values as iterable. Nested Dictionaries with for loop Access Nested values of Nested Dictionaries How useful was this post? Click on a star to rate it! Submit Rating
For Loops with python
For loop is one of the most useful methods to reuse a code for repetitive execution.
Metrics and terminologies of digital analytics
These all metrics are revolving around visits and hits which we are getting on websites. Single page visits, Bounce, Cart Additions, Bounce Rate, Exit rate,
Hypothesis Testing
Hypothesis testing is a statistical method for determining whether or not a given hypothesis is true. A hypothesis can be any assumption based on data.
A/B testing
A/B tests are randomly controlled experiments. In A/B testing, you get user response on various versions of the product, and users are split within multiple versions of the product to figure out the “winner” of the version.
For Loop With Tuples
This article covers ‘for’ loops and how they are used with tuples. Even if the tuples are immutable, the accessibility of the tuples is similar to that of the list.
Multivariate ANOVA (MANOVA) with python
MANOVA is an update of ANOVA, where we use a minimum of two dependent variables.
Two-Way ANOVA
You only need to understand two or three concepts if you have read the one-way ANOVA article. We use two factors instead of one in a two-way ANOVA.
One response to “Introduction to Pandas: A Guide”
[…] Pandas […]
Points You Earned