Python data analysis: let the data speak for you

Import Data Use python's pandas library to easily import data in a variety of formats, including CSV, excel and sqldatabase.

import pandas as pd
df = pd.read_csv("data.csv")

Data Exploration Data exploration capabilities help you quickly understand the distribution and trends of data. Use the describe() method to view statistics on the data, and the head() method to preview the first few rows.

print(df.describe())
print(df.head())

Data Cleaning Data cleaning is an important step in ensuring data accuracy and consistency. Python Provides various tools, such as fillna() and drop_duplicates() methods, for handling missing values ​​and duplicate records.

df.fillna(0, inplace=True)
df.drop_duplicates(inplace=True)

data visualization Data visualization is an effective way to communicate insights and discover patterns. The Matplotlib and Seaborn libraries provide a variety of charts and diagrams for creating interactive and eye-catching visualization effects.

import matplotlib.pyplot as plt
df.plot(kind="bar")# 创建柱状图
plt.show()

Machine Learning Python's Scikit-learn library makes machine learningalgorithms easily accessible. You can use a variety of supervised and unsupervised learning algorithms to predict, classify, or cluster data.

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X, y)# 训练模型

For more advanced analysis, you can use distributed computing frameworks such as Dask and spark. These frameworks can handle large-scale data sets and significantly improve performance through parallel processing.

import dask.dataframe as dd
ddf = dd.from_pandas(df, npartitions=4)# 创建分布式数据框