9 Pandas Tricks That Data Scientists Use Quietly

1. Use df.query() for clean filtering

import pandas as pd
df=pd.DataFrame({
    "name":["Alice","Bob","Charlie"],
    "age": [24,34,26]
})

df.query("age>25")
#source code --> clcoding.com 

Output:

name
age
1
Bob
34
2
Charlie
26

2. Use df.assign() to add column

df=pd.DataFrame({"A":[1,2,3]})
df=df.assign(B=df.A*10)
df
#source code --> clcoding.com 

Output:
A
B
0
1
10
1
2
20
2
3
30
3. Use df.rename() with dictionary
df=df.rename(columns={"A":"col_A","B":"col_B"})
df
#source code --> clcoding.com 
Output:
col_A
col_B
0
1
10
1
2
20
2
3
30
4. Use df.nlargest() to get top n values

df = pd.DataFrame({"score": [50, 90, 80, 95]})
df.nlargest(2, "score")


#source code --> clcoding.com

Output:
score
3
95
1
90
5. Ise df.sample() to pick random rows

df.sample(2)

#source code --> clcoding.com 

Output:
score
2
80
0
50
6. Use df.drop_duplicates() to clean data

df = pd.DataFrame({"A":[1,1,2,2,3], "B":[10,10,20,20,30]})
df.drop_duplicates()

#source code --> clcoding.com 
Output:
A
B
0
1
10
2
2
20
4
3
30
7. Use df.to_clipboard() to copy
df.to_clipboard(index=False)
df
#source code --> clcoding.com 

Output:
A
B
0
1
10
1
1
10
2
2
20
3
2
20
4
3
30
8. Use df.style() for quick visual highlight

df = pd.DataFrame({
    "name": ["A", "B", "C"],
    "score": [50, 90, 75]
})
df.style.highlight_max("score")

#source code --> clcoding.com 
Output:
 
name
score
0
A
50
1
B
90
2
C
75
9. Use df.memory_usage() to see memory footprint
df.memory_usage()

#source code --> clcoding.com 

Output:
Index    132
name      24
score     24
dtype: int64