Data Operations using Pandas and Numpy

Import Libraries

# Import the Pandas and NumPy libraries
import pandas as pd
import numpy as np

Import the required libraries before proceeding with data operations.

Create DataFrame

# Create a DataFrame using Pandas
data = {
    'Column1': [1, 2, 3],
    'Column2': ['A', 'B', 'C']
}
df = pd.DataFrame(data)

Use a dictionary to define data and create a DataFrame with Pandas.

Read Data

# Read data from a CSV file into a DataFrame
df = pd.read_csv('file_path.csv')

Replace 'file_path.csv' with the actual file path to load data into Pandas DataFrame.

Write Data

# Write DataFrame data to a CSV file
df.to_csv('output_path.csv', index=False)

Write the contents of the DataFrame to a CSV file without including row indices.

Create NumPy Array

# Create a NumPy array
np_array = np.array([1, 2, 3])

Create a simple NumPy array with some values.

DataFrame Operations

# Basic operations on DataFrames
# Selecting a column
column1 = df['Column1']

# Adding a new column
df['Column3'] = df['Column1'] * 2

# Filtering rows
filtered_df = df[df['Column1'] > 1]

Perform selection, column addition, and row filtering on the DataFrame.

NumPy Operations

# Basic operations on NumPy arrays
# Element-wise addition
result_add = np_array + 10

# Conditional selection
result_conditional = np_array[np_array > 1]

# Array reshaping
reshaped_array = np_array.reshape((3, 1))

Perform element-wise addition, conditional selection, and reshaping on the NumPy array.

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