Detailed steps for using Python module for data processing

pip install pandas

import pandas as pd

# Create a DataFramedata = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = (data)

# View DataFrameprint(df)

# Data cleaning# Delete duplicate linesdf.drop_duplicates(inplace=True)

# Fill in missing values(value={"Age": 0, "City": "Unknown"}, inplace=True)

# Data Filteryoung_people = df[df["Age"] < 30]
print(young_people)

# Data sortingsorted_df = df.sort_values(by="Age", ascending=False)
print(sorted_df)

# Data aggregationaverage_age = df["Age"].mean()
print(f"Average Age: {average_age}")

# Data Exportdf.to_csv("", index=False)

pip install numpy

import numpy as np

# Create a NumPy arraydata = ([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# View arrayprint(data)

# Numerical calculationmean_value = (data)
print(f"Mean Value: {mean_value}")

# Array Slicingsub_array = data[1:, :2]
print(sub_array)

# Array Operationdata_squared = data ** 2
print(data_squared)

# Data Export("", data, fmt="%d")

pip install matplotlib

import  as plt

# Create datax = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Draw a line chart(x, y, label="Line 1")
("Line Plot Example")
("X-axis")
("Y-axis")
()
()

# Draw a bar chartcategories = ["A", "B", "C", "D", "E"]
values = [10, 15, 7, 12, 20]

(categories, values, color="skyblue")
("Bar Chart Example")
("Categories")
("Values")
()

pip install scikit-learn

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from  import mean_squared_error
import numpy as np

# Create dataX = ([[1], [2], [3], [4], [5]])
y = ([2, 4, 6, 8, 10])

# Divide the training set and the test setX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a linear regression modelmodel = LinearRegression()

# Train the model(X_train, y_train)

# Make predictionsy_pred = (X_test)

# Evaluate the modelmse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

import pandas as pd
import  as plt

# Create a DataFramedata = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = (data)

# Data cleaningdf.drop_duplicates(inplace=True)
(value={"Age": 0, "City": "Unknown"}, inplace=True)

# Data Filteryoung_people = df[df["Age"] < 30]

# Data sortingsorted_df = df.sort_values(by="Age", ascending=False)

# Data visualization(figsize=(10, 6))
(sorted_df["Name"], sorted_df["Age"], color="skyblue")
("Age Distribution")
("Name")
("Age")
()