C__Users_dell_Downloads_archive.py

#!/usr/bin/env python
# coding: utf-8

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


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data=pd.read_csv('C:/Users/dell/Downloads/archive/IMDB Top 250 Movies.csv')


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data


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data.head()


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data.tail()


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data.head(10)


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data.tail(10)


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data.shape


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print("Number of rows",data.shape[0])
print("Number of columns",data.shape[1])


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data.info()


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print(data.isnull().values.any())


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data.isnull().sum()


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data


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sns.heatmap(data.isnull())


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dup_data=data.duplicated().any()


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print("any duplicate values",dup_data)


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data.drop_duplicates()
data


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data.describe(include='all')


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data.columns


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data.groupby('year')['rating'].mean().sort_values(ascending=False)


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sns.barplot(x='rating',y='year',data=data)
plt.title("Ratings by year")
plt.show()


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data.columns


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data.groupby('rank')['rating'].mean().sort_values(ascending=False)


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sns.barplot(x='rating',y= 'rank',data=data)
plt.title("Ratings by rank")
plt.show()


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data.columns


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top10_len=data.nlargest(10,'rank')[['name','rank']].set_index('name')


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top10_len


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sns.barplot(x='rank',y=top10_len.index,data=top10_len)


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data['year'].value_counts()


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sns.countplot(x='year',data=data)


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top10_len=data.nlargest(10,'rank')[['name','rank','directors']].set_index('name')


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top10_len


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sns.barplot(x='rank',y=top10_len.index,data=top10_len,hue='directors',dodge=False)
plt.legend(bbox_to_anchor=(1.05,1),loc=2)


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