2nd way
import pandas as pd
# Example DataFrame
data = {
"A": [1, 2, 3, 4, 5],
"B": [2, 4, 6, 8, 10],
"C": [5, 4, 3, 2, 1],
"D": [10, 20, 30, 40, 50]
}
df = pd.DataFrame(data)
# Correlation matrix
corr = df.corr()
# Filter correlations > 0.5 (excluding self-correlation = 1.0)
filtered = corr[(corr > 0.5) & (corr < 1.0)]
print("Correlation > 0.5:")
print(filtered)
🟢 Output Example
Correlation > 0.5:
A B C D
A NaN 1.0 NaN 1.0
B 1.0 NaN NaN 1.0
C NaN NaN NaN NaN
D 1.0 1.0 NaN NaN
import java.util.*;
// Given Interface (Leetcode style)
interface NestedInteger {
boolean isInteger();
Integer getInteger();
List<NestedInteger> getList();
}
// Implementation for testing
class NI implements NestedInteger {
private Integer value;
private List<NestedInteger> list;
public NI(Integer val) {
this.value = val;
this.list = null;
}
public NI(List<NestedInteger> list) {
this.list = list;
this.value = null;
}
@Override
public boolean isInteger() {
return value != null;
}
@Override
public Integer getInteger() {
return value;
}
@Override
public List<NestedInteger> getList() {
return list;
}
}
// Iterator class
class NestedIterator implements Iterator<Integer> {
private Queue<Integer> q = new LinkedList<>();
public NestedIterator(List<NestedInteger> nestedList) {
flatten(nestedList);
}
private void flatten(List<NestedInteger> nestedList) {
for (NestedInteger ni : nestedList) {
if (ni.isInteger()) {
q.add(ni.getInteger());
} else {
flatten(ni.getList());
}
}
}
@Override
public Integer next() {
return q.poll();
}
@Override
public boolean hasNext() {
return !q.isEmpty();
}
}
public class Main {
public static void main(String[] args) {
// Input: [[1,1], 2, [1,1]]
List<NestedInteger> input = new ArrayList<>();
input.add(new NI(Arrays.asList(new NI(1), new NI(1))));
input.add(new NI(2));
input.add(new NI(Arrays.asList(new NI(1), new NI(1))));
NestedIterator i = new NestedIterator(input);
while (i.hasNext()) {
System.out.print(i.next() + " ");
}
}
}
kaise use karna Bhai .apply() Pandas ka most used aur most powerful function hai — ye tab use hota hai jab:
Tum har row ya har column pe koi custom operation lagana chahte ho — especially jab woh operation ek simple loop se slow ho jata.
Ab main tujhe .apply() ka use learnable format mein sikhata hoon — taaki logic bhi samjhe, aur yaad bhi rahe.
.apply() on a column (Series pe)📌 Use case: Ek column ke sabhi values ka square nikalna
import pandas as pd
df = pd.DataFrame({
'number': [1, 2, 3, 4, 5]
})
df['square'] = df['number'].apply(lambda x: x**2)
print(df)
📤 Output:
number square
0 1 1
1 2 4
2 3 9
3 4 16
4 5 25
🧠Learn this logic:
df['col'].apply(function)= apply this function to each value of the column
.apply() with a custom functiondef label_even_odd(x):
return "even" if x % 2 == 0 else "odd"
df['type'] = df['number'].apply(label_even_odd)
📤 Output:
number type
0 1 odd
1 2 even
...
🧠Learn this logic:
Define function → Apply on column → Return new values
.apply() on multiple columns (axis=1)📌 Use case: Combine first & last name
df = pd.DataFrame({
'first': ['Rahul', 'Priya'],
'last': ['Sharma', 'Verma']
})
df['full_name'] = df.apply(lambda row: row['first'] + ' ' + row['last'], axis=1)
📤 Output:
first last full_name
0 Rahul Sharma Rahul Sharma
1 Priya Verma Priya Verma
🧠Logic:
df.apply(lambda row: ..., axis=1)= Apply function on each row
.apply() + if conditions📌 Use case: Category tagging
df = pd.DataFrame({'marks': [90, 60, 40, 30]})
df['grade'] = df['marks'].apply(lambda x: 'A' if x >= 75 else ('B' if x >= 50 else 'C'))
📤 Output:
marks grade
0 90 A
1 60 B
2 40 C
...
🧠Logic:
Use nested
if-elseinside lambda to apply conditions
.apply() on list column📌 Use case: Length of strings in list
df = pd.DataFrame({'words': ['hello', 'world', 'pandas', 'rocks']})
df['length'] = df['words'].apply(len)
📤 Output:
words length
0 hello 5
1 world 5
...
🧠Logic:
.apply(len)→ len is a function → used on each cell
| Use Case | Code Example |
|---|---|
| Column pe function | df['col'].apply(lambda x: x*2) |
| Custom func | df['col'].apply(my_func) |
| Multiple columns | df.apply(lambda row: ..., axis=1) |
| Condition | apply(lambda x: 'yes' if x > 50 else 'no') |
| List pe apply | df['list_col'].apply(len) |
Try writing this:
👉 Maan lo ek DataFrame hai:
df = pd.DataFrame({'temperature': [30, 40, 50]})
Tumhe ek naya column banana hai:
'status' = 'hot' if temp >= 40 else 'normal'
Try this using .apply() and lambda. Agar chaahe toh bhej, main verify kar dunga.
Aur bhai agar tu .applymap() or .map() ka confusion chahta clear karna, toh woh bhi kara deta hoon side-by-side comparison se.
1. subh leetcode
2. and deep learning ka resnet and all
1 baje so gaya main
3. 5 baje se dsa ka ek vidoe dekha 6 baje tak
wasted 2 hour bich mein
4. 8 baje maine start kiya deep learning coursera ka
ye kiya badiya
leetcode ka ek question
5 .raat ko leetcode ke question mein time lag agay
6. discrte maths
1. Subh ka time do video discrete
2. backprop ka ek video
then waste hi hu
3. shaam ko 5 bje se
1. deep learing campsx wale ka 3 video dekhe
2. leetcode 1 question
3. Data structure and algo -: atelast 1 video
2nd way import pandas as pd # Example DataFrame data = { "A": [1, 2, 3, 4, 5], "B": [2, 4, 6, 8, 10], ...