Day 48 of Data Analytics Pandas & Java Arrays !

Today, I explored Pandas in Python and Arrays in Java. Here's what I learned and practiced:

1. Assigning Index in Pandas

You can set a custom index for a DataFrame instead of the default numeric index.

import pandas as pd

data = {'Name': ['Raj', 'Ashok', 'Aruna'], 'Age': [39, 20, 20]}
df = pd.DataFrame(data)
df.index = ['a', 'b', 'c']
print(df)

Output:

      Name  Age
a      Raj   39
b    Ashok   20
c    Aruna   20

2. Resetting Index

Reset index to default numeric values.

df_reset = df.reset_index()
print(df_reset)

Output:

  index    Name  Age
0     a     Raj   39
1     b   Ashok   20
2     c   Aruna   20

3. Accessing Data

Access a single column or row:

print(df['Name'])   # Access column
print(df.loc['b'])  # Access row with index 'b'

4. Filtering & Slicing

Filter rows based on condition:

print(df[df['Age'] > 20])  # Age greater than 20

Slice rows:

print(df[0:2])  # First 2 rows

5. True or False Filtering

Check conditions for each row:

print(df['Age'] > 20)

Output:

a     True
b    False
c    False
Name: Age, dtype: bool

6. Capitalize First Letter of Each Word

sentence = "this is my today contente make it for post"
capitalized_sentence = sentence.title()
print(capitalized_sentence)

Output:

This Is My Today Contente Make It For Post

7. Arrays in Java

Basic usage of arrays in Java:

public class ArrayExample {
    public static void main(String[] args) {
        int[] numbers = {10, 20, 30, 40};

        // Accessing array elements
        System.out.println(numbers[2]);  // 30

        // Looping through array
        for(int num : numbers){
            System.out.println(num);
        }
    }
}

Output:

Today’s focus was on data manipulation in Python using Pandas and understanding arrays in Java. These are fundamental skills for data analytics and programming.