7 Lesser-Known Pandas Functions That’ll Blow Your Mind

1. explode()=Turns list items into rows

import pandas as pd

df = pd.DataFrame({"Name": ["A", "B"], 
                   "Tags": [["x", "y"], ["p", "q"]]})
df.explode("Tags")

#source code --> clcoding.com

Output:

Name
Tags
0
A
x
0
A
y
1
B
p
1
B
q

2. query()- Filter rows using expression

df = pd.DataFrame({"Age":[20,25,30], "Score":[90,85,70]})
df.query("Age > 22 and Score > 80")

#source code --> clcoding.com
Output:
Age
Score
1
25
85
3. nlargets()- Get the top n rows

df=pd.DataFrame({"Name":["A","B","C"],"Marks":[50,95,80]})
df.nlargest(1,"Marks")
#source code --> clcoding.com
Output:
Name
Marks
1
B
95
4. nsmallest()- Get the lowest n rows

df=pd.DataFrame({"A":[10,3,7],"B":[4,9,1]})
df.nsmallest(2,"A")
#source code --> clcoding.com
Output:
A
B
1
3
9
2
7
1


5. pivot_table()- Create summary table automatically

df=pd.DataFrame({
    "City":["A","A","B","B"],
    "Sales": [10,20,30,5]
})
df.pivot_table(values="Sales",index="City",aggfunc="sum")

#source code --> clcoding.com
Output:
Sales
City
A
30
B
35
6. fillna(method="ffill")- Fills missing values forward

df = pd.DataFrame({"X":[1,None,None,4]})
df.fillna(method="ffill")

#source code --> clcoding.com
Output:
X
0
1.0
1
1.0
2
1.0
3
4.0
7. assign()- Adds new column cleanly

df = pd.DataFrame({"A":[1,2,3]})
df = df.assign(B=df.A * 10)
print(df)

#source code --> clcoding.com
Output:
   A   B
0  1  10
1  2  20
2  3  30