1. NumPy: Numpy is a package library used for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, as well as a range of mathematical functions to operate on these data structures.

Example code:

import numpy as np

# Creating a numpy array

arr = np.array([1, 2, 3, 4])

# Using numpy functions to operate on the array

print(np.mean(arr)) # Returns the mean value

print(np.std(arr)) # Returns the standard deviation

2. Matplotlib: Matplotlib is a package library used for creating data visualizations in Python. It provides a range of functions to create graphs, plots, and charts, as well as customization options for colors, labels, and other visual elements.

Example code:

import matplotlib.pyplot as plt

# Creating a simple line plot

x_values = [1, 2, 3, 4]

y_values = [1, 4, 9, 16]

plt.plot(x_values, y_values)

# Customizing the plot with labels and title

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Example Line Plot')

3. Pandas: Pandas is a package library used for data analysis and manipulation in Python. It provides tools for reading, cleaning, and transforming data, as well as options for merging, grouping, and aggregating data sets.

Example code:

import pandas as pd

# Creating a pandas data frame from a CSV file

data = pd.read_csv('example_data.csv')

# Using pandas functions to manipulate the data

print(data.head()) # Shows the first 5 rows of the data frame

print(data.describe()) # Provides summary statistics for the data

4. Scikit-Learn: Scikit-learn is a package library used for machine learning in Python. It provides tools for data preprocessing, model selection, and performance evaluation, as well as a range of supervised and unsupervised learning algorithms.

Example code:

import sklearn

from sklearn.linear_model import LinearRegression

# Creating a linear regression model

model = LinearRegression()

# Fitting the model to data

x_values = [[1, 2], [2, 4], [3, 6], [4, 8]]

y_values = [2, 4, 6, 8]

model.fit(x_values, y_values)

# Using the model to make predictions

print(model.predict([[5, 10], [6, 12], [7, 14]])) # Predicting y values for new x values

Example code:

import numpy as np

# Creating a numpy array

arr = np.array([1, 2, 3, 4])

# Using numpy functions to operate on the array

print(np.mean(arr)) # Returns the mean value

print(np.std(arr)) # Returns the standard deviation

2. Matplotlib: Matplotlib is a package library used for creating data visualizations in Python. It provides a range of functions to create graphs, plots, and charts, as well as customization options for colors, labels, and other visual elements.

Example code:

import matplotlib.pyplot as plt

# Creating a simple line plot

x_values = [1, 2, 3, 4]

y_values = [1, 4, 9, 16]

plt.plot(x_values, y_values)

# Customizing the plot with labels and title

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Example Line Plot')

3. Pandas: Pandas is a package library used for data analysis and manipulation in Python. It provides tools for reading, cleaning, and transforming data, as well as options for merging, grouping, and aggregating data sets.

Example code:

import pandas as pd

# Creating a pandas data frame from a CSV file

data = pd.read_csv('example_data.csv')

# Using pandas functions to manipulate the data

print(data.head()) # Shows the first 5 rows of the data frame

print(data.describe()) # Provides summary statistics for the data

4. Scikit-Learn: Scikit-learn is a package library used for machine learning in Python. It provides tools for data preprocessing, model selection, and performance evaluation, as well as a range of supervised and unsupervised learning algorithms.

Example code:

import sklearn

from sklearn.linear_model import LinearRegression

# Creating a linear regression model

model = LinearRegression()

# Fitting the model to data

x_values = [[1, 2], [2, 4], [3, 6], [4, 8]]

y_values = [2, 4, 6, 8]

model.fit(x_values, y_values)

# Using the model to make predictions

print(model.predict([[5, 10], [6, 12], [7, 14]])) # Predicting y values for new x values

Frequently Used Methods

Frequently Used Methods

Frequently Used Methods

Related

Related in langs