Python data analysis: Insight into the patterns behind your data-Python Tutorial-php.cn

Python data analysis: Insight into the patterns behind your data

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Release： 2024-02-19 14:30:18

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Python data analysis: Insight into the patterns behind your data

Data analytics has become an integral part of modern business, helping companies extract valuable insights from data and make informed decisions. Python is a powerful programming language with an extensive data analysis library, making it one of the preferred tools for data analysis.

data processing

Pandas: A high-level library for data processing and manipulation. Easily load, clean, transform and merge data sets.

import pandas as pd

# 加载 CSV 文件
df = pd.read_csv("data.csv")

# 清洗和准备数据
df = df.dropna()# 删除缺失值
df["column"] = df["column"].astype("cateGory")# 转换数据类型

# 合并数据集
df2 = pd.read_csv("data2.csv")
df = pd.merge(df, df2, on="id")

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NumPy: A library for scientific computing. Provides efficient numerical array processing, very suitable for large data sets.

import numpy as np

# 创建一个 NumPy 数组
arr = np.array([1, 2, 3, 4, 5])

# 数组操作
arr_mean = np.mean(arr)# 计算平均值
arr_sum = np.sum(arr)# 计算总和

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data visualization

Matplotlib: A library for creating a variety of charts and graphs. Can generate histograms, scatter plots, line charts, etc.

import matplotlib.pyplot as plt

# 创建一个散点图
plt.scatter(df["x"], df["y"])
plt.xlabel("x")
plt.ylabel("y")
plt.show()

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Seaborn: An advanced visualization library built on Matplotlib. Provides more advanced chart types and styles.

import seaborn as sns

# 创建一个热力图
sns.heatmap(df.corr())# 计算相关矩阵并绘制热力图
plt.show()

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Data Mining and Machine Learning

Scikit-learn: An extensive library for machine learning. Provides various classification, regression and clustering algorithms.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# 划分训练和测试集
X_train, X_test, y_train, y_test = train_test_split(df[["x", "y"]], df["z"])

# 训练线性回归模型
model = LinearRegression()
model.fit(X_train, y_train)

# 评估模型
score = model.score(X_test, y_test)# 计算准确率

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TensorFlow: A powerful deep learningframework. Can be used to build neural networks, process natural language and computer vision tasks.

import Tensorflow as tf

# 创建一个神经网络模型
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dense(1, activation="sigmoid")
])

# 训练模型
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(X_train, y_train, epochs=10)

# 评估模型
loss, accuracy = model.evaluate(X_test, y_test)

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Advantages of Python data analysis

Powerful tools: Python has a series of powerful data analysis libraries that can meet various data processing, visualization and machine learning needs.
Easy to use: Python is a language with concise syntax and strong readability, which lowers the threshold for data analysis.
Active community: Python has a large and active community that provides documentation, tutorials, and support.
Scalability: Python provides a scalable platform for large data sets and complex analysis tasks.

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