How to reduce a data with the longest string under pandas framework?

How to reduce a data with the longest string under pandas framework?

By : user2956017
Date : November 22 2020, 10:48 AM
I hope this helps . The code should work. BTW, you don't need to wrap f1 inside another lambda. Just pass f1. (They have exactly same parameter signature)
code :
>>> import pandas as pd
>>> def f1(s):
...     return max(s, key=len)
>>> data = pd.DataFrame([
...     {'id': 'GB', 'name': '"United Kingdom"'},
...     {'id': 'GB', 'name': 'England'},
...     {'id': 'US', 'name': '"United States"'},
...     {'id': 'US', 'name': 'America'},
... ])
>>> data.groupby('id').agg({'name': f1})
GB  "United Kingdom"
US   "United States"

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Find length of longest string in Pandas dataframe column

Find length of longest string in Pandas dataframe column

By : G-Wiz
Date : March 29 2020, 07:55 AM
it fixes the issue DSM's suggestion seems to be about the best you're going to get without doing some manual microoptimization:
code :
%timeit -n 100 df.col1.str.len().max()
100 loops, best of 3: 11.7 ms per loop

%timeit -n 100 df.col1.map(lambda x: len(x)).max()
100 loops, best of 3: 16.4 ms per loop

%timeit -n 100 df.col1.map(len).max()
100 loops, best of 3: 10.1 ms per loop
How do I count the longest consecutive '0' flanked by number '1' in is string using pandas dataframe

How do I count the longest consecutive '0' flanked by number '1' in is string using pandas dataframe

By : user2964407
Date : March 29 2020, 07:55 AM
help you fix your problem You can try of using string related operations with split and count
Convert column to string -> followed by split with '1' and counting the max
code :
df['result'] = df.label.astype(str).str.split('1').apply(lambda x: len(max(x)))
   Id      label  result
0   1   1         0
1   2   11        0
2   3   101       1
3   4   10101     1
4   5   1001      2
Pandas - Find longest streak of string values in column together with row id

Pandas - Find longest streak of string values in column together with row id

By : user3384393
Date : March 29 2020, 07:55 AM
hope this fix your issue We can use Series.cumsum + Series.shift to create groups according to consecutive names(see detail). Then you can use GroupBy.agg to create a dataframe with the size of each group. ,the first index and datetime value of each group. Sort the dataframe by size using DataFrame.sort_values ​​and remove duplicates (You can use DataFrame.drop_duplicates) to remove groups with the same name and smaller size. Convert the columns to str. (You may need to convert Datetime also if your actual data is not str). Then you can use Series.str.cat to join the columns. Finally, we can use Series.to_dict + DataFrame.set_index to obtaind the dictionary
code :
df_agg= (   df.groupby(groups,sort=False).agg(Name=('Name','first'),

df_agg['j']=df_agg['j1'].str.cat(df_agg['Datemin'],sep=' or ')

        Name  length  idxmin Datemin   j1             j
4     Esther       3       4   Date5  3,4  3,4 or Date5
1     Harald       2       0   Date1  2,0  2,0 or Date1
3      Steve       1       3   Date4  1,3  1,3 or Date4
{'Esther': '3,4 or Date5', 'Harald': '2,0 or Date1', 'Steve': '1,3 or Date4'}

0    1
1    1
2    2
3    3
4    4
5    4
6    4
Name: Name, dtype: int64
pandas dataframe longest series with uninterrupted data

pandas dataframe longest series with uninterrupted data

By : Jos Arturo Llanquihu
Date : March 29 2020, 07:55 AM
I wish this help you You can first make a temporary DataFrame in which each series of uninterrupted data is labeled with a (per column) unique number. And place the 'original' NaN's back so the longest series cant be a series of NaN's.
How to find the longest consecutive string of values in pandas dataframe

How to find the longest consecutive string of values in pandas dataframe

By : Sagar bobade
Date : March 29 2020, 07:55 AM
hop of those help? I'm looking to find the longest string of zeros in my pandas df. I have a df array with 10 columns, each with 25000 rows that have either a null, a zero or a non-zero number. I am looking to calculate: , Setup
Consider the dataframe df
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