ok

Author: machphy

pandas/mere_dost.csv

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

pandas/new.py

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

pandas/pandfas.py

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