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Commit5c4085d

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‎pandas/mere_dost.csv

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name,marks,city
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ajay,45,gorakhpur
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vijay,10,kushingar
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mohan,42,kasia
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jadish,45,lucknow
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krishna,36,odisha

‎pandas/new.py

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importpandasaspd
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# Load dataset (replace 'population.csv' with the path to your file)
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df=pd.read_csv('population.csv')
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# 1. Inspect the Data
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print("First 5 rows of the dataset:")
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print(df.head())
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print("\nBasic Information:")
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print(df.info())
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print("\nSummary Statistics:")
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print(df.describe())
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# 2. Check for Missing Data
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print("\nMissing Values per Column:")
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print(df.isnull().sum())
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# 3. Handle Missing Data (Example: Fill with mean or drop)
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df_cleaned=df.fillna(df.mean())# Fill missing values with column means
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# Alternatively, drop rows with missing values
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# df_cleaned = df.dropna()
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# 4. Data Analysis - Grouping and Aggregation
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# Example: Group by a column and calculate the mean of other columns
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grouped_data=df_cleaned.groupby('Category').mean()
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print("\nMean values by Category:")
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print(grouped_data)
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# 5. Filter Data
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# Example: Filter rows where a column 'Sales' is greater than 500
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filtered_data=df_cleaned[df_cleaned['Sales']>500]
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print("\nFiltered Data (Sales > 500):")
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print(filtered_data)
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# 6. Correlation Analysis
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print("\nCorrelation between numerical columns:")
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print(df_cleaned.corr())
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# 7. Save the cleaned data to a new CSV file
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df_cleaned.to_csv('cleaned_data.csv',index=False)
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# Optional: Plotting (if you want to visualize the data)
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importmatplotlib.pyplotasplt
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df_cleaned['Sales'].hist(bins=20)
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plt.title('Sales Distribution')
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plt.xlabel('Sales')
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plt.ylabel('Frequency')
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plt.show()

‎pandas/pandfas.py

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importnumpyasnp
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importpandasaspd
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dict={
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"name":['ajay','vijay','mohan','jadish','krishna'],
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"marks":['45','10','42','45','36'],
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"city":['gorakhpur','kushingar','kasia','lucknow','odisha ']
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}
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# data farama
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df=pd.DataFrame(dict)
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df.to_csv('mere_dost.csv')
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# for index need no need
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df.to_csv('mere_dost.csv',index=False)
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df.head(2)
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df.tail(2)
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df.describe()
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print(df.describe)

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