Python Scripts For Beginners: Data Handling

Collection of 100 Python Scripts For Beginners related to Data Handling, each designed to help with fundamental tasks and provide useful examples.

1. Reading a CSV File

import pandas as pd

df = pd.read_csv('data.csv')
print(df.head())

Reads a CSV file into a DataFrame and prints the first few rows.

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2. Writing Data to a CSV File

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data)
df.to_csv('output.csv', index=False)

Creates a DataFrame from a dictionary and writes it to a CSV file.

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3. Filtering Rows in a DataFrame

import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['Age'] > 30]
print(filtered_df)

Filters rows where the ‘Age’ column is greater than 30.

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4. Sorting Data in a DataFrame

import pandas as pd

df = pd.read_csv('data.csv')
sorted_df = df.sort_values(by='Age', ascending=False)
print(sorted_df)

Sorts the DataFrame by the ‘Age’ column in descending order.

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5. Aggregating Data

import pandas as pd

df = pd.read_csv('data.csv')
mean_age = df['Age'].mean()
print(f"Mean Age: {mean_age}")

Calculates the mean age from the ‘Age’ column.

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6. Merging Two DataFrames

import pandas as pd

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
merged_df = pd.merge(df1, df2, on='ID')
print(merged_df)

Merges two DataFrames on the ‘ID’ column.

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7. Concatenating DataFrames

import pandas as pd

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
concatenated_df = pd.concat([df1, df2])
print(concatenated_df)

Concatenates two DataFrames vertically.

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8. Handling Missing Data

import pandas as pd

df = pd.read_csv('data.csv')
df = df.fillna(method='ffill')  # Forward fill missing values
print(df)

Fills missing values using forward fill method.

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9. Dropping Missing Data

import pandas as pd

df = pd.read_csv('data.csv')
df = df.dropna()
print(df)

Drops rows with any missing values.

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10. Renaming Columns

import pandas as pd

df = pd.read_csv('data.csv')
df = df.rename(columns={'old_name': 'new_name'})
print(df)

Renames columns in the DataFrame.

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11. Changing Data Types

import pandas as pd

df = pd.read_csv('data.csv')
df['Age'] = df['Age'].astype(int)
print(df.dtypes)

Changes the data type of the ‘Age’ column to integer.

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12. Removing Duplicates

import pandas as pd

df = pd.read_csv('data.csv')
df = df.drop_duplicates()
print(df)

Removes duplicate rows from the DataFrame.

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13. Data Aggregation with GroupBy

import pandas as pd

df = pd.read_csv('data.csv')
grouped_df = df.groupby('Department').agg({'Salary': 'mean'})
print(grouped_df)

Groups data by ‘Department’ and calculates the mean ‘Salary’ for each group.

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14. Plotting Data

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data.csv')
df['Age'].plot(kind='hist')
plt.show()

Plots a histogram of the ‘Age’ column.

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15. Pivot Table Creation

import pandas as pd

df = pd.read_csv('data.csv')
pivot_table = df.pivot_table(values='Salary', index='Department', aggfunc='mean')
print(pivot_table)

Creates a pivot table to show the average ‘Salary’ by ‘Department’.

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16. Reading Excel Files

import pandas as pd

df = pd.read_excel('data.xlsx')
print(df.head())

Reads data from an Excel file into a DataFrame.

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17. Writing Data to an Excel File

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data)
df.to_excel('output.xlsx', index=False)

Writes a DataFrame to an Excel file.

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18. Parsing JSON Data

import pandas as pd

df = pd.read_json('data.json')
print(df.head())

Reads JSON data into a DataFrame.

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19. Writing Data to JSON

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data)
df.to_json('output.json', orient='records')

Writes a DataFrame to a JSON file.

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20. Reading SQL Data into DataFrame

import pandas as pd
import sqlite3

conn = sqlite3.connect('database.db')
df = pd.read_sql_query('SELECT * FROM table_name', conn)
print(df.head())

Reads SQL data into a DataFrame.

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21. Writing DataFrame to SQL

import pandas as pd
import sqlite3

conn = sqlite3.connect('database.db')
data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data)
df.to_sql('table_name', conn, if_exists='replace', index=False)

Writes a DataFrame to an SQL table.

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22. Filtering Data Based on Condition

import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['Salary'] > 50000]
print(filtered_df)

Filters rows where ‘Salary’ is greater than 50,000.

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23. Extracting Substrings

import pandas as pd

df = pd.read_csv('data.csv')
df['First_Name'] = df['Full_Name'].str.split().str[0]
print(df)

Extracts the first name from a ‘Full_Name’ column.

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24. Handling Dates

import pandas as pd

df = pd.read_csv('data.csv', parse_dates=['Date'])
print(df['Date'].dt.year)

Converts a column to datetime and extracts the year.

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25. Data Normalization

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

df = pd.read_csv('data.csv')
scaler = MinMaxScaler()
df[['Age']] = scaler.fit_transform(df[['Age']])
print(df)

Normalizes the ‘Age’ column to a range of [0, 1].

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26. Data Standardization

import pandas as pd
from sklearn.preprocessing import StandardScaler

df = pd.read_csv('data.csv')
scaler = StandardScaler()
df[['Age']] = scaler.fit_transform(df[['Age']])
print(df)

Standardizes the ‘Age’ column to have a mean of 0 and standard deviation of 1.

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27. Handling Categorical Data

import pandas as pd

df = pd.read_csv('data.csv')
df = pd.get_dummies(df, columns=['Department'])
print(df)

Converts categorical variables into dummy/indicator variables.

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28. Combining Multiple CSV Files

import pandas as pd
import glob

files = glob.glob('data/*.csv')
dfs = [pd.read_csv(file) for file in files]
combined_df = pd.concat(dfs, ignore_index=True)
print(combined_df)

Combines multiple CSV files into a single DataFrame.

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29. Creating a DataFrame from a Dictionary

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data)
print(df)

Creates a DataFrame from a dictionary.

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30. Saving DataFrame to a Pickle File

import pandas as pd

df = pd.read_csv('data.csv')
df.to_pickle('data.pkl')

Saves a DataFrame to a pickle file.

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31. Loading DataFrame from a Pickle File

import pandas as pd

df = pd.read_pickle('data.pkl')
print(df)

Loads a DataFrame from a pickle file.

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32. Converting DataFrame to Dictionary

import pandas as pd

df = pd.read_csv('data.csv')
data_dict = df.to_dict()
print(data_dict)

Converts a DataFrame to a dictionary.

34. Creating a DataFrame with MultiIndex

import pandas as pd

arrays = [['A', 'A', 'B', 'B'], ['x', 'y', 'x', 'y']]
index = pd.MultiIndex.from_arrays(arrays, names=('Letter', 'Number'))
df = pd.DataFrame({'Value': [1, 2, 3, 4]}, index=index)
print(df)

Creates a DataFrame with multi-level indexing.

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35. Calculating Summary Statistics

import pandas as pd

df = pd.read_csv('data.csv')
summary = df.describe()
print(summary)

Calculates and prints summary statistics for numerical columns.

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36. Handling Text Data

import pandas as pd

df = pd.read_csv('data.csv')
df['Text_Length'] = df['Text_Column'].apply(len)
print(df)

Calculates the length of text in a column.

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37. Merging DataFrames on Multiple Columns

import pandas as pd

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
merged_df = pd.merge(df1, df2, on=['ID', 'Date'])
print(merged_df)

Merges DataFrames on multiple columns.

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38. Applying Functions to DataFrame Columns

import pandas as pd

df = pd.read_csv('data.csv')
df['Age_In_5_Years'] = df['Age'].apply(lambda x: x + 5)
print(df)

Applies a function to each element of a column.

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39. Creating Pivot Tables

import pandas as pd

df = pd.read_csv('data.csv')
pivot_table = df.pivot_table(values='Sales', index='Region', columns='Month', aggfunc='sum')
print(pivot_table)

Creates a pivot table to aggregate data based on regions and months.

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40. Converting DataFrame to a List of Dictionaries

import pandas as pd

df = pd.read_csv('data.csv')
list_of_dicts = df.to_dict(orient='records')
print(list_of_dicts)

Converts a DataFrame to a list of dictionaries.

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41. Normalizing Data

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

df = pd.read_csv('data.csv')
scaler = MinMaxScaler()
df[['Value']] = scaler.fit_transform(df[['Value']])
print(df)

Normalizes a column to a [0, 1] range.

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42. Standardizing Data

pythonCopier le codeimport pandas as pd
from sklearn.preprocessing import StandardScaler

df = pd.read_csv('data.csv')
scaler = StandardScaler()
df[['Value']] = scaler.fit_transform(df[['Value']])
print(df)

Standardizes a column to have mean 0 and standard deviation 1.

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43. Converting Data Types

import pandas as pd

df = pd.read_csv('data.csv')
df['Age'] = df['Age'].astype(float)
print(df.dtypes)

Changes the data type of a column.

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44. Handling Outliers

import pandas as pd

df = pd.read_csv('data.csv')
q1 = df['Value'].quantile(0.25)
q3 = df['Value'].quantile(0.75)
iqr = q3 - q1
filtered_df = df[(df['Value'] >= (q1 - 1.5 * iqr)) & (df['Value'] <= (q3 + 1.5 * iqr))]
print(filtered_df)

Filters outliers from a DataFrame using the IQR method.

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45. Aggregating Data with GroupBy

import pandas as pd

df = pd.read_csv('data.csv')
grouped_df = df.groupby('Category').agg({'Value': 'sum'})
print(grouped_df)

Groups data by ‘Category’ and calculates the sum of ‘Value’.

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46. Splitting Data into Training and Testing Sets

import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_csv('data.csv')
train, test = train_test_split(df, test_size=0.2)
print(train.head())
print(test.head())

Splits data into training and testing sets.

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47. Reading Data from a JSON File

import pandas as pd

df = pd.read_json('data.json')
print(df.head())

Reads JSON data into a DataFrame.

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48. Writing DataFrame to JSON File

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data)
df.to_json('output.json', orient='records')

Writes a DataFrame to a JSON file.

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49. Handling Time Series Data

import pandas as pd

df = pd.read_csv('data.csv', parse_dates=['Date'])
df.set_index('Date', inplace=True)
print(df.resample('M').sum())

Handles time series data and aggregates it by month.

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50. Adding Calculated Columns

import pandas as pd

df = pd.read_csv('data.csv')
df['Discounted_Price'] = df['Price'] * 0.9
print(df)

Adds a new column with a calculated value.

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51. Combining DataFrames Horizontally

import pandas as pd

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
combined_df = pd.concat([df1, df2], axis=1)
print(combined_df)

Combines two DataFrames side-by-side (horizontally).

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52. Extracting Specific Columns

import pandas as pd

df = pd.read_csv('data.csv')
df_subset = df[['Name', 'Age']]
print(df_subset)

Extracts specific columns from a DataFrame.

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53. Removing Unnecessary Columns

import pandas as pd

df = pd.read_csv('data.csv')
df = df.drop(columns=['Unnecessary_Column'])
print(df)

Removes unnecessary columns from a DataFrame.

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54. Handling Categorical Data with Encoding

import pandas as pd
from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('data.csv')
encoder = LabelEncoder()
df['Category'] = encoder.fit_transform(df['Category'])
print(df)

Encodes categorical data into numerical format.

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55. Saving DataFrame to a Database

import pandas as pd
import sqlite3

conn = sqlite3.connect('database.db')
df = pd.read_csv('data.csv')
df.to_sql('table_name', conn, if_exists='replace', index=False)

Saves a DataFrame to an SQL database.

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56. Reading Data from a Database

import pandas as pd
import sqlite3

conn = sqlite3.connect('database.db')
df = pd.read_sql_query('SELECT * FROM table_name', conn)
print(df.head())

Reads data from a database into a DataFrame.

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57. Filling Missing Values with Interpolation

import pandas as pd

df = pd.read_csv('data.csv')
df = df.interpolate()
print(df)

Fills missing values using interpolation.

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58. Exporting Data to Excel

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data)
df.to_excel('output.xlsx', index=False)

Exports a DataFrame to an Excel file.

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59. Reading Excel Sheets

import pandas as pd

df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
print(df.head())

Reads a specific sheet from an Excel file.

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60. Appending Data to an Existing CSV

import pandas as pd

new_data = {'Name': ['Tom'], 'Age': [30]}
new_df = pd.DataFrame(new_data)
new_df.to_csv('data.csv', mode='a', header=False, index=False)

Appends new data to an existing CSV file.

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61. Deleting Rows Based on Condition

import pandas as pd

df = pd.read_csv('data.csv')
df = df[df['Age'] > 25]
print(df)

Deletes rows where ‘Age’ is less than or equal to 25.

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62. Sampling Data

import pandas as pd

df = pd.read_csv('data.csv')
sample_df = df.sample(n=10)
print(sample_df)

Samples 10 random rows from a DataFrame.

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63. Resampling Time Series Data

import pandas as pd

df = pd.read_csv('data.csv', parse_dates=['Date'])
df.set_index('Date', inplace=True)
monthly_data = df.resample('M').mean()
print(monthly_data)

Resamples time series data to monthly frequency and calculates the mean.

64. Handling Large Datasets with Chunks

import pandas as pd

chunk_size = 10000
chunks = pd.read_csv('large_data.csv', chunksize=chunk_size)

for chunk in chunks:
    process(chunk)  # Replace with your processing function

Reads and processes large datasets in chunks to manage memory usage.

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65. Removing Extra Whitespace

import pandas as pd

df = pd.read_csv('data.csv')
df.columns = df.columns.str.strip()
df['Name'] = df['Name'].str.strip()
print(df)

Removes extra whitespace from column names and data.

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66. Checking for Missing Data

import pandas as pd

df = pd.read_csv('data.csv')
missing_data = df.isnull().sum()
print(missing_data)

Checks for and counts missing data in each column.

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67. Filling Missing Data with a Value

import pandas as pd

df = pd.read_csv('data.csv')
df = df.fillna(value={'Age': 30})
print(df)

Fills missing values in the ‘Age’ column with 30.

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68. Converting Column to Categorical Data

import pandas as pd

df = pd.read_csv('data.csv')
df['Category'] = df['Category'].astype('category')
print(df.dtypes)

Converts a column to categorical data type.

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69. Creating a DataFrame from a List of Tuples

pimport pandas as pd

data = [('John', 28), ('Anna', 24)]
df = pd.DataFrame(data, columns=['Name', 'Age'])
print(df)

Creates a DataFrame from a list of tuples.

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70. Creating a DataFrame with Custom Index

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Age': [28, 24]}
df = pd.DataFrame(data, index=['Person1', 'Person2'])
print(df)

Creates a DataFrame with a custom index.

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71. Applying Multiple Functions to Columns

import pandas as pd

df = pd.read_csv('data.csv')
df['Age_Square'] = df['Age'].apply(lambda x: x ** 2)
df['Age_Log'] = df['Age'].apply(lambda x: np.log(x))
print(df)

Applies multiple functions to a column and creates new columns.

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72. Generating Random Data

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': np.random.choice(['John', 'Anna', 'Tom'], size=100),
    'Age': np.random.randint(20, 40, size=100)
})
print(df)

Generates a DataFrame with random data.

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73. Removing Outliers Using Z-Score

import pandas as pd
from scipy import stats

df = pd.read_csv('data.csv')
df = df[(np.abs(stats.zscore(df['Value'])) < 3)]
print(df)

Removes outliers based on the Z-score.

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74. Creating a Histogram from Data

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data.csv')
df['Age'].hist()
plt.show()

Creates a histogram of the ‘Age’ column.

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75. Creating a DataFrame from JSON

import pandas as pd

df = pd.read_json('data.json')
print(df)

Creates a DataFrame from a JSON file.

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76. Saving DataFrame to a Pickle File

import pandas as pd

df = pd.read_csv('data.csv')
df.to_pickle('data.pkl')

Saves a DataFrame to a pickle file.

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77. Reading Data from a Pickle File

import pandas as pd

df = pd.read_pickle('data.pkl')
print(df)

Loads a DataFrame from a pickle file.

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78. Merging DataFrames with Different Column Names

import pandas as pd

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
merged_df = pd.merge(df1, df2, left_on='ID', right_on='Identifier')
print(merged_df)

Merges DataFrames on columns with different names.

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79. Pivoting Data

import pandas as pd

df = pd.read_csv('data.csv')
pivot_df = df.pivot(index='Date', columns='Category', values='Value')
print(pivot_df)

Creates a pivot table from a DataFrame.

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80. Creating a MultiIndex DataFrame

import pandas as pd

arrays = [['A', 'A', 'B', 'B'], ['x', 'y', 'x', 'y']]
index = pd.MultiIndex.from_arrays(arrays, names=('Letter', 'Number'))
df = pd.DataFrame({'Value': [1, 2, 3, 4]}, index=index)
print(df)

Creates a DataFrame with multi-level indexing.

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81. Filtering Data with Regular Expressions

import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['Email'].str.contains('@gmail.com')]
print(filtered_df)

Filters data using regular expressions to match email domains.

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82. Extracting Columns from a DataFrame

import pandas as pd

df = pd.read_csv('data.csv')
columns = df[['Name', 'Age']]
print(columns)

Extracts specific columns from a DataFrame.

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83. Removing Duplicates from Data

import pandas as pd

df = pd.read_csv('data.csv')
df = df.drop_duplicates()
print(df)

Removes duplicate rows from a DataFrame.

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84. Filling Missing Data with Mean

import pandas as pd

df = pd.read_csv('data.csv')
mean_value = df['Value'].mean()
df['Value'] = df['Value'].fillna(mean_value)
print(df)

Fills missing values in a column with the mean value.

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85. Creating DataFrame from SQL Query

import pandas as pd
import sqlite3

conn = sqlite3.connect('database.db')
df = pd.read_sql_query('SELECT * FROM table_name', conn)
print(df)

Reads data from a SQL database into a DataFrame.

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86. Aggregating Data with Multiple Functions

import pandas as pd

df = pd.read_csv('data.csv')
agg_df = df.groupby('Category').agg({'Value': ['mean', 'sum', 'max']})
print(agg_df)

Aggregates data using multiple functions.

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87. Handling Dates and Times

import pandas as pd

df = pd.read_csv('data.csv', parse_dates=['Date'])
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
print(df)

Extracts year and month from date columns.

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88. Exporting Data to Multiple Sheets in Excel

import pandas as pd

with pd.ExcelWriter('output.xlsx') as writer:
    df1 = pd.read_csv('data1.csv')
    df2 = pd.read_csv('data2.csv')
    df1.to_excel(writer, sheet_name='Sheet1', index=False)
    df2.to_excel(writer, sheet_name='Sheet2', index=False)

Exports multiple DataFrames to different sheets in an Excel file.

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89. Using DataFrame with SQLAlchemy

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine('sqlite:///database.db')
df = pd.read_sql('SELECT * FROM table_name', engine)
print(df)

Reads data from a SQL database using SQLAlchemy.

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90. Handling Large Datasets with Dask

import dask.dataframe as dd

df = dd.read_csv('large_data.csv')
df = df.compute()  # Convert to pandas DataFrame
print(df.head())

Handles large datasets using Dask.

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91. Cleaning Text Data

import pandas as pd

df = pd.read_csv('data.csv')
df['Text_Cleaned'] = df['Text_Column'].str.replace('[^a-zA-Z]', '')
print(df)

Removes non-alphabetic characters from text data.

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92. Resampling Data by Week

import pandas as pd

df = pd.read_csv('data.csv', parse_dates=['Date'])
df.set_index('Date', inplace=True)
weekly_data = df.resample('W').sum()
print(weekly_data)

Resamples data to weekly frequency and calculates the sum.

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93. Handling Large JSON Files

import pandas as pd

df = pd.read_json('large_data.json', lines=True)
print(df.head())

Reads large JSON files into a DataFrame.

94. Converting DataFrame to Excel with Formatting

import pandas as pd
from openpyxl import Workbook
from openpyxl.styles import Font

df = pd.read_csv('data.csv')
with pd.ExcelWriter('output.xlsx', engine='openpyxl') as writer:
    df.to_excel(writer, index=False, sheet_name='Sheet1')
    workbook = writer.book
    worksheet = writer.sheets['Sheet1']
    
    # Apply formatting
    for cell in worksheet["A1:Z1"]:  # Adjust range as needed
        cell.font = Font(bold=True, color="FFFFFF")
    worksheet.sheet_view.showGridLines = False

Writes a DataFrame to an Excel file and applies basic formatting.

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95. Adding Conditional Formatting in Excel

import pandas as pd
from openpyxl import load_workbook
from openpyxl.formatting.rule import CellIsRule
from openpyxl.styles import PatternFill

df = pd.read_csv('data.csv')
with pd.ExcelWriter('output.xlsx', engine='openpyxl') as writer:
    df.to_excel(writer, index=False, sheet_name='Sheet1')
    
    workbook = writer.book
    worksheet = writer.sheets['Sheet1']
    
    # Add conditional formatting
    rule = CellIsRule(operator='greaterThan', formula=['100'], fill=PatternFill(start_color='FFFF00', end_color='FFFF00', fill_type='solid'))
    worksheet.conditional_formatting.add('B2:B100', rule)  # Adjust range as needed

Applies conditional formatting to an Excel sheet.

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96. Using SQL to Filter Data

import pandas as pd
import sqlite3

conn = sqlite3.connect('database.db')
query = "SELECT * FROM table_name WHERE Age > 30"
df = pd.read_sql_query(query, conn)
print(df)

Filters data using an SQL query.

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97. Combining DataFrames with Concatenation

import pandas as pd

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
combined_df = pd.concat([df1, df2], axis=0, ignore_index=True)
print(combined_df)

Concatenates two DataFrames vertically.

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98. Aggregating Data Using GroupBy and Multiple Functions

import pandas as pd

df = pd.read_csv('data.csv')
agg_df = df.groupby('Category').agg({
    'Value': ['mean', 'sum', 'max'],
    'Age': 'min'
})
print(agg_df)

Aggregates data using multiple functions for different columns.

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99. Filtering Data Based on Multiple Conditions

import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[(df['Age'] > 25) & (df['Category'] == 'A')]
print(filtered_df)

Filters rows based on multiple conditions.

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100. Creating a DataFrame from a Dictionary of Series

import pandas as pd

data = {
    'Name': pd.Series(['John', 'Anna']),
    'Age': pd.Series([28, 24])
}
df = pd.DataFrame(data)
print(df)

This collection of 100 Python scripts for beginners is designed to enhance your understanding of data handling with practical examples.