源码：用Python进行电影数据分析实战指南-EW帮帮网

源码：用Python进行电影数据分析实战指南

原创 IT小本本 IT小本本 2025年03月03日 22:28 北京

1、首先复制csv内容到csv文件中

2、接着创建.py文件复制源码内容

3、运行代码，就可以看到数据分析图啦

源码内容：

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# 1. 加载数据
def load_data(file_path):
    """
    从CSV文件加载电影数据集
    """
    df = pd.read_csv(file_path)
    print("数据维度:", df.shape)
    print("\n前5行数据:")
    print(df.head())
    print("\n数据摘要:")
    print(df.info())
    return df

# 2. 数据清洗
def clean_data(df):
    """
    数据清洗预处理
    """
    # 删除重复值
    df = df.drop_duplicates()
    
    # 处理缺失值
    df['rating'] = df['rating'].fillna(df['rating'].median())
    df = df.dropna(subset=['release_year', 'genre'])
    
    # 转换数据类型
    df['release_year'] = df['release_year'].astype(int)
    
    # 处理genre列（可能有多个类型）
    df['genre'] = df['genre'].str.split(',')
    
    return df

# 3. 数据分析
def analyze_data(df):
    """
    执行数据分析并生成可视化
    """
    # 设置可视化风格
    sns.set(style="whitegrid")
    plt.figure(figsize=(12, 6))
    
    # 分析1：电影类型分布
    genre_counts = df.explode('genre')['genre'].value_counts().head(10)
    plt.subplot(2, 2, 1)
    genre_counts.plot(kind='bar', color='skyblue')
    plt.title('Top 10 Movie Genres')
    plt.xlabel('Genre')
    plt.ylabel('Count')

    # 分析2：评分分布
    plt.subplot(2, 2, 2)
    sns.histplot(df['rating'], bins=20, kde=True, color='orange')
    plt.title('Rating Distribution')
    plt.xlabel('IMDB Rating')

    # 分析3：年度电影数量趋势
    yearly_counts = df.groupby('release_year').size()
    plt.subplot(2, 2, 3)
    yearly_counts.plot(color='green')
    plt.title('Movies Released by Year')
    plt.xlabel('Year')
    plt.ylabel('Number of Movies')

    # 分析4：评分与时长关系
    plt.subplot(2, 2, 4)
    sns.scatterplot(x='runtime', y='rating', data=df, alpha=0.6)
    plt.title('Runtime vs Rating')
    plt.xlabel('Runtime (minutes)')
    plt.ylabel('Rating')

    plt.tight_layout()
    plt.show()

    # 高级分析：相关系数矩阵
    numeric_df = df.select_dtypes(include=['float64', 'int64'])
    plt.figure(figsize=(10, 8))
    sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm')
    plt.title('Correlation Matrix')
    plt.show()

# 主程序
if __name__ == "__main__":
    # 文件路径（需要替换为实际路径）
    file_path = "movies.csv"
    
    # 加载数据
    movie_df = load_data(file_path)
    
    # 数据清洗
    cleaned_df = clean_data(movie_df)
    
    # 数据分析
    analyze_data(cleaned_df)
    
    # 生成统计摘要
    print("\n统计摘要:")
    print(cleaned_df.describe())

csv内容：


title	rating	genre	release_year	runtime	votes	director	actors
Ne Zha: The Demon Boy Makes Havoc in the Sea	9.8	Animation	2025	144		Jiaozi
Product quite	5.1	Horror,Animation	1998	154		Wesley Weaver	Sheila Blackburn, Christina Harris, Jacob Odonnell
Choose support stuff	8	Action	1995	87	316631		Brian Vance, Karen Norris, Thomas West
Field itself growth	8.2	Thriller,Horror,Adventure	2007	163	398833	Daniel Kelly	Kenneth Jackson, Allen Campbell MD, Stacy Andersen
Task available president	6.7	Horror	2011	73			James Bishop, Rachel Williams, Cameron Wilson
Fly system event	8	Adventure	1997	178	290428	David Crawford	Randall Gonzalez, Larry Collins, Emily Sullivan
Evidence	9.4	Animation,Horror	1994	65	406310	Robert Lucas	Jeremiah Robinson, Megan Williams, Megan Herrera
Treat week		Comedy	2014	95	438314	Mitchell Dickson	Hailey Richardson, Nancy Davis, Cynthia Luna MD
Step staff	6	Comedy,Drama	2005	80	142530	Rebecca Wilson	Patrick Thompson, Amy Hernandez, Christopher King
Much such	7.8	Romance,Action,Horror	1992	163	141373		Kenneth Wang, April Avila, Adam Singleton
Wish water	7.8	Romance,Horror,Adventure	2015	173	241570	John Poole	Dr. Dennis Ryan, Vincent Valdez, John Rose
Two	8.7	Sci-Fi,Thriller,Documentary	2013	88	73525	Justin Turner	Andrew Coffey, Robin Jarvis, Daniel Murray
Someone song		Comedy	1999	140		Lisa Atkinson	James Brown, Cynthia Lopez, Jennifer Lopez
Culture quality	6.9	Adventure,Documentary,Sci-Fi	1990	147	358412	Michael Garrison	Robert Jenkins, Peter Combs, Charles Marsh DDS
Each listen and	8.8	Comedy,Sci-Fi	2017	80	379584	Michael Murphy	Rachel Reeves, David Matthews, Miss Dawn Hayes
Particular	7.5	Documentary	1996	171	442277	Rebecca Bryant DDS	Cody Cain, Dillon Powell, Kelsey Riley
Control lawyer	5.8	Documentary	2023	177		Chad Brown	Susan Morales, Michael Mann, Brian Hunter
Performance yourself then		Sci-Fi,Horror	1993	90	497474		Christopher Knapp, Edward Chapman PhD, Steven Richardson
Clearly	9.1	Drama,Animation,Adventure	2000	93	319029	Cynthia Harrison	Rodney Patterson, Shawn Wells, David Hill
Risk town	7.7	Horror,Sci-Fi,Thriller	2013	116	237708	Suzanne Smith	James Williams, Francisco Miller, Scott Herman
Once structure	6.7	Documentary,Horror,Animation	1990	69	31866	Mr. Jonathan Stafford	Pamela Johnson, John Rodriguez, Misty Wells
Condition morning	9.3	Documentary	2005	95	113321	Robert Jennings	James Williams, Antonio Zuniga, Adam Stewart
Lawyer almost method	9.4	Adventure,Horror,Animation	2016	84		Katherine Clark	Joshua Bernard, Jeffrey James, Cheryl Salinas
Central write	7.1	Action,Comedy,Animation	2015	153	452764	Joe Hernandez	Matthew Donaldson, Jennifer Kelley, Leslie Gomez
Apply window	7.7	Thriller	2001	95	459482	Logan Williams	Brooke Bruce, Danielle Dixon, Michael Burton
Trouble benefit another	9.3	Comedy,Action,Documentary	1995	178	124930	Christina Wood	Darren Jones, Brian Fischer, Paula Garcia
Represent career away	6.1	Action,Drama	2014	60	140042	Terri Melendez	Deanna Walker, Joseph Robinson, Mrs. Samantha Mccarthy
Product money	6.4	Documentary,Adventure,Drama	2002	97	31662	Paul Hale	Rachel Taylor, Lisa Hughes, Christopher Jordan
Standard campaign hot	8.7	Action	2020	124	279463	Evan Holmes	Madison Sanchez, Rachel Smith, Hannah Avery
Foreign care	5.4	Comedy	1994	146	212694	Alexander Morgan	Bill Doyle, Mary Garrison, Barbara Velazquez
Our of force	6.2	Action,Comedy,Thriller	2023	100	108090	Erica Mack	Cheryl Ray, Bobby Webster, Philip Mcdonald
Moment poor hour	6.3	Sci-Fi	2019	100	491594		Tyler Smith, Crystal Grimes, Amanda Watson
Science suffer human	10	Comedy	2003	124	490296	Nicole Evans	Karen Cook, Albert Tate, Teresa Watkins
Possible mission		Romance	2008	80	285802	Kyle Vasquez	Sonia Stanley, Dr. Olivia Sullivan, Tony Garcia
Factor difficult short	9.9	Documentary,Action,Sci-Fi	1996	180	139656	Terry Rogers	Felicia Dunn, Victor Spencer, Robert Mcdonald
Ready organization	7.8	Sci-Fi,Horror	2011	86	448171	Robert Green	Andrew Robinson, David Baker, Erik Jones
Right phone standard	9.5	Comedy	2017	166	2858	Kristin Montes	Thomas Martin, Amanda West, Patrick Travis
Start painting	9.9	Adventure	2017	131			Jesus Green, Robert Davis, Rebecca Davis
Whose member area	9.5	Action,Romance,Drama	2017	76		Mr. Martin Garcia	Tiffany Williams, Karen Ramirez, Lauren Matthews
Teach however	9.9	Romance	2003	147	54893	Melvin Medina	Shannon Bell, Jeffrey Hoffman, Samantha Walton
Within response one book	9.4	Drama,Horror,Animation	2001	172	95036	Latoya Petersen	Christina Pearson, Shawn Hart, Joseph Moore
Nation south debate	9.7	Horror,Sci-Fi,Drama	1996	108	451150	Paul Clark	Darrell Neal, Patrick Durham, Nathan Freeman
Method town firm	9.6	Sci-Fi,Horror	2004	63	209910	John King	Renee Williams, James Hunter, Lindsey Buchanan
Produce movie	9.8	Romance,Documentary	2015	146	282131	Daniel Diaz	Ashley Lara, Dustin Pearson, John Franklin
Best across		Drama,Sci-Fi	1992	73	182239	Debra Calderon	Deborah Hunter DVM, Peter Phillips, Donna Wright
Pass crime	8	Comedy,Thriller,Horror	2006	65	1886	Yolanda Baxter	Adam Hood, Edwin Henderson, Stephen Anderson
Move	8.4	Horror,Romance,Thriller	1994	145	327712	Roy Schwartz	Joann Fleming, Maria Simpson, John Mason
Where cause idea	8.6	Documentary,Sci-Fi	2016	101	156008	Wesley Turner	Dr. Carol Diaz, Daniel Santana, Tina White
Water concern	8.3	Drama,Sci-Fi	2015	130	160317	Nicole Martin	Corey Sanders, Rebecca Tran, Kari Mason
Degree you	7.9	Romance,Adventure,Documentary	2018	146	249085	Nicholas Lawson	Brian Robbins, Charles Schwartz, Shawn Ramos
Wonder firm pull	8.7	Comedy,Sci-Fi,Romance	1995	164	124138	Kelly Thomas	Holly Stark, Susan Bishop, Adam Perez

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源码：用Python进行电影数据分析实战指南

源码：用Python进行电影数据分析实战指南

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