How to extract max length row with pandas - python-3.x

Question

I would like to extract row which is max in Dataframe.
In following case, I would like to get id 2 row, because it includes max length 6 in B column bbbbbb.
|id|A |B |
|1 |abc |aaa |
|2 |abb |bbbbbb|
|3 |aadd|cccc |
|4 |aadc|ddddd |
|id|A |B |
|2 |abb |bbbbbb|
Please give me some advice. Thanks.

Answer 1

Let's first create the DataFrame with you example:
import pandas as pd
data = {
"id": {0: 1, 1: 2, 2: 3, 3: 4},
"A ": {0: "abc", 1: "abb", 2: "aadd", 3: "aadc"},
"B": {0: "aaa", 1: "bbbbbb", 2: "cccc", 3: "ddddd"}
}
df = pd.DataFrame(data)
Then you can get the row where B is longer and then retrive that row with:
# Index where B is longest
idx = df["B"].apply(len).idxmax()
# Get that row
df.iloc[idx, :]

Answer 2

Get all columns filled by object (obviously strings) by DataFrame.select_dtypes, get length with max per rows and last filter maximal by boolean indexing for match all rows with maximal lengths:
s = df.select_dtypes(object).apply(lambda x: x.str.len()).max(axis=1)
#if no missing values
#s = df.select_dtypes(object).applymap(len).max(axis=1)
df1 = df[s.eq(s.max())]
print (df1)
id A B
1 2 abb bbbbbb
Another idea for only first match by Series.idxmax and DataFrame.loc, added [] for one row DataFrame:
df1 = df.loc[[df.select_dtypes(object).apply(lambda x: x.str.len()).max(axis=1).idxmax()]]
#if no missing values
#df1 = df.loc[[df.select_dtypes(object).applymap(len).max(axis=1).idxmax()]]
print (df1)
id A B
1 2 abb bbbbbb

Answer 3

First, you can find the maximal length per each row and then the row index with a maximal value:
df.loc[df[['A', 'B']].apply(lambda x: x.str.len().max(), axis=1).idxmax()]