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Ramya .C
Ramya .C

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📊 Day 49 of My Data Analytics Journey !

#datascience #beginners #devjournal #python

Today I learned four important Pandas DataFrame operations:
Sorting, Merge, Join, and Concat.

These are essential for organizing and combining data efficiently. Let’s explore each one 👇

🔹 1. Sorting

Definition:
Sorting is used to arrange data in ascending or descending order based on one or more columns.

Types of Sorting:

  • Ascending Sort: Small to large (default)
  • Descending Sort: Large to small
  • By Index Sort: Sort based on index values
  • By Column Sort: Sort based on specific column(s)

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Ram', 'Anu', 'Kavi'],
    'Age': [25, 22, 30]
})

# Sort by Age in ascending order
sorted_df = df.sort_values(by='Age')

# Sort by Age in descending order
sorted_desc = df.sort_values(by='Age', ascending=False)

print(sorted_df)
print(sorted_desc)
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🔹 2. Merge

Definition:
The merge() function combines two DataFrames based on common columns or keys — similar to SQL joins.

Types of Merge:

  • Inner Join: Returns only matching rows from both DataFrames
  • Left Join: Returns all rows from the left DataFrame and matching rows from the right
  • Right Join: Returns all rows from the right DataFrame and matching from the left
  • Outer Join: Returns all rows from both DataFrames, filling missing values with NaN

Example:

df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Ram', 'Anu', 'Kavi']})
df2 = pd.DataFrame({'ID': [1, 2, 4], 'Marks': [85, 90, 88]})

merged_df = pd.merge(df1, df2, on='ID', how='inner')
print(merged_df)
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🔹 3. Join

Definition:
join() is used to combine two DataFrames based on their index or a key column.

Types of Join:

  • Inner Join
  • Left Join
  • Right Join
  • Outer Join

Example:

df1 = pd.DataFrame({'Name': ['Ram', 'Anu', 'Kavi']}, index=[1, 2, 3])
df2 = pd.DataFrame({'Marks': [85, 90, 88]}, index=[1, 2, 3])

joined_df = df1.join(df2)
print(joined_df)
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🔹 4. Concat

Definition:
concat() is used to combine two or more DataFrames either vertically (rows) or horizontally (columns).

Types of Concat:

  • Vertical Concat (axis=0): Add rows
  • Horizontal Concat (axis=1): Add columns

Example:

df1 = pd.DataFrame({'Name': ['Ram', 'Anu']})
df2 = pd.DataFrame({'Name': ['Kavi', 'Sara']})

# Combine rows
concat_rows = pd.concat([df1, df2], axis=0)

# Combine columns
df3 = pd.DataFrame({'Marks': [85, 90]})
concat_cols = pd.concat([df1, df3], axis=1)

print(concat_rows)
print(concat_cols)
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💡 Key Takeaway:
These functions help you manipulate, combine, and organize datasets — essential skills for every data analyst working with real-world data!

#Day49 #DataAnalytics #Python #Pandas #Sorting #Join #Merge #Concat #100DaysOfCode #LearningJourney

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