pandas dataframe python with examples

 

What is a Pandas DataFrame?

A Pandas DataFrame is like a table in a spreadsheet or SQL database. It consists of rows and columns, where:

• Rows are observations or data points.
• Columns are variables or attributes associated with those observations.

DataFrames are flexible and can hold data of different types (integers, floats, strings, etc.), making them suitable for data manipulation and analysis.

 

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Creating a Pandas DataFrame

 

You can create a Pandas DataFrame in several ways:

 

• From a dictionary
• From a list of lists or arrays
• From a CSV file
  

 

Create Pandas DataFrame From a Python Dictionary:

 

import pandas as pd

# create Pandas DataFrame From a Python Dictionary:
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}

df = pd.DataFrame(data)
print(df)
    

 

Output:

 

                 Name  Age     City
0             chandra   25  chennai
1               kumar   30  vellore
2  dailyaspirants.com    5   Mumbai
    

 

This dictionary, data, contains three keys: 'Name', 'Age', and 'City'. Each key has a list of values representing data for each person.

 

The pd.DataFrame(data) function converts the dictionary into a Pandas DataFrame. Each key becomes a column, and each list item becomes a row.

 

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Accessing Data in a DataFrame

 

Accessing data in a DataFrame is straightforward:

• Use df['column_name'] to access a column.
• Use df.iloc[row_index] or df.loc[row_label] to access rows.
  

import pandas as pd

# create Pandas DataFrame From a Python Dictionary:
data = {
    'Name': ['chandra', 'kumar', 'dailyaspirants.com'],
    'Age': [25, 30, 5],
    'City': ['chennai', 'vellore', 'Mumbai']
}

df = pd.DataFrame(data)
# Accessing the 'Name' column
names = df['Name']
print(names)

# Accessing the first row
first_row = df.iloc[0]
print(first_row)
    

Output:

0               chandra
1                 kumar
2    dailyaspirants.com
Name: Name, dtype: object
Name    chandra
Age          25
City    chennai
Name: 0, dtype: object
    

 

Modifying a DataFrame

 

You can easily modify the content of a DataFrame:

• Adding a column: df['New_Column'] = [values]
• Dropping a column: df.drop('column_name', axis=1, inplace=True)
• Updating values: df.at[row_index, 'column_name'] = new_value

 

Output:

                 Name           City  Salary
0             chandra        chennai   50000
1               kumar  San Francisco   60000
2  dailyaspirants.com         Mumbai   70000
    

 

Pandas DataFrame From a Python List

 

Using Python to create a DataFrame using a two-dimensional list. For example:

 

import pandas as pd

# create Pandas DataFrame From a Python Dictionary:
data = {
    'Name': ['chandra', 'kumar', 'dailyaspirants.com'],
    'Age': [25, 30, 5],
    'City': ['chennai', 'vellore', 'Mumbai']
}

# create a DataFrame from the list
df = pd.DataFrame(data, columns=['Name', 'Age', 'City'])

print(df)
    

Filtering Data in a DataFrame

 

Filtering data is one of the most useful operations when analyzing data. You can filter data based on specific conditions using Pandas.

 

import pandas as pd

# create Pandas DataFrame From a Python Dictionary:
data = {
    'Name': ['chandra', 'kumar', 'dailyaspirants.com'],
    'Age': [25, 30, 5],
    'City': ['chennai', 'vellore', 'Mumbai']
}

df = pd.DataFrame(data)
# Adding a new column
df['Salary'] = [50000, 60000, 70000]

# Updating a value
df.at[1, 'City'] = 'San Francisco'

# Dropping the 'Age' column
df.drop('Age', axis=1, inplace=True)

# Filtering rows where Salary is greater than 55000
high_salary = df[df['Salary'] > 55000]
print(high_salary)
    

 

Output:

 

                 Name           City  Salary
1               kumar  San Francisco   60000
2  dailyaspirants.com         Mumbai   70000
    

 

Importing and Exporting Data with DataFrames

 

Pandas makes it simple to import and export data in various formats, such as CSV, Excel, JSON, and more.

• Reading a CSV file: pd.read_csv('file.csv')
• Writing to a CSV file: df.to_csv('file.csv', index=False)

# Reading data from a CSV file
df = pd.read_csv('data.csv')

# Writing the DataFrame to a new CSV file
df.to_csv('new_data.csv', index=False)