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No changes to original dataframe after applying loop


No changes to original dataframe after applying loop

By : tatman poon
Date : October 23 2020, 08:10 AM
help you fix your problem That's because the variable df in your for loop does not reference a value in your list. You are creating a variable df afresh each iteration of your loop.
You can assign via enumerate and pipe your function:
code :
for idx, df in enumerate(df_lst):
    df_lst[idx] = df.pipe(dropzeros)


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R use of loop for applying a function to ove variable in a dataframe

R use of loop for applying a function to ove variable in a dataframe


By : eld0727
Date : March 29 2020, 07:55 AM
I hope this helps you . More of a crude way than using the package. Split by 5, and apply median to the list.
code :
x <- as.data.frame(seq(0,500, by=10))

set.seed(1234)

y <- as.data.frame(rnorm(51,38,2))

df <- data.frame(x,y)
colnames(df) = c("time", "temp")

a = split(df, rep(1:11 , each = 5))
res = sapply(a, function(x) {median(x$temp)})
Expand a Dictionary into DataFrame then Add to Original DataFrame, with New Columns, and Copied Original Data

Expand a Dictionary into DataFrame then Add to Original DataFrame, with New Columns, and Copied Original Data


By : junivive2
Date : March 29 2020, 07:55 AM
Does that help Here is one way....I still think you can using the better constructor to reach your expected output.
code :
original.set_index('b').a.apply(pd.Series).stack().\
    reset_index(name='text').rename(columns={'level_1':'numbers'})
    Out[1623]: 
       b  numbers   text
    0  1        1     hi
    1  1        2  there
original.set_index('position')['news_results'].apply(pd.Series).stack().apply(pd.Series).reset_index()
Out[1633]: 
   position level_1               title                                url
0         1       1         Worst case   https://www.politico.com/magazine
1         1       2          Bad Night         https://www.nbcnews.com/pol
2         1       3  On the anniversary      https://www.usatoday.com/stor
3         2       1    Bad Things Happ        https://www.nytimes.com/maga
4         2       2         Best Night            https://www.cnn.com/pols
Applying grouped pandas data back to the original dataframe

Applying grouped pandas data back to the original dataframe


By : rijil12
Date : March 29 2020, 07:55 AM
may help you . I have the dataframe below that I am working with: , Your function doesn’t have a return statement
Applying values to a DataFrame without using a for-loop

Applying values to a DataFrame without using a for-loop


By : intellectassets
Date : March 29 2020, 07:55 AM
With these it helps I'm looking for a faster method of applying values to a column in a DataFrame. The value is based on two True and False values in the first and second column. This is my current solution: , Use pandas.DataFrame.groupby.cumsum:
code :
import pandas as pd

df['result'] = df.groupby(df['check1'].cumsum())[['check1', 'check2']].cumsum().sum(1)
df['result'] = df.groupby(df['check1'].cumsum())['check2'].cumsum().add(1)
    check1  check2  result
0     True   False     1.0
1    False   False     1.0
2    False   False     1.0
3    False   False     1.0
4    False   False     1.0
5    False   False     1.0
6    False    True     2.0
7    False   False     2.0
8    False    True     3.0
9    False   False     3.0
10   False    True     4.0
11   False   False     4.0
12   False    True     5.0
13   False   False     5.0
14   False    True     6.0
15   False   False     6.0
16   False    True     7.0
17   False   False     7.0
18   False   False     7.0
19   False   False     7.0
20   False    True     8.0
21   False   False     8.0
22   False    True     9.0
23    True   False     1.0
24   False   False     1.0
Create a list of dataframes that are generated by applying a function to subsets of an original dataframe

Create a list of dataframes that are generated by applying a function to subsets of an original dataframe


By : elefk
Date : March 29 2020, 07:55 AM
may help you . I'm trying to create a list of dataframes that have been created by applying a function to subsets of my original dataframe. , An option could be
code :
library(dplyr)

result_by_country <- group_by(Data, Country) %>% 
  summarise(outcome_table = list(table(Outcome))) 
Countries <- result_by_country$outcome_table
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