Combine Dataframe rows on conditions

Combine Dataframe rows on conditions

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Combine Dataframe rows on conditions
Tag : python , By : kiirpi
Date : November 28 2020, 11:01 PM

I hope this helps . sort_values groupby('ID_1') track STOP_D.cummax() and see if START_D is less than prior cummax cumsum to generate groupings agg to grab min START_D and max STOP_D
df_example = df.sort_values(['START_D', 'STOP_D'])

def collapse(df):
    s, e = 'START_D', 'STOP_D'
    grps = df[s].gt(df[e].cummax().shift()).cumsum()
    funcs = {s: 'min', e: 'max', 'ID_1': 'first'}
    return df.groupby(grps).agg(funcs)


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How to subset a dataframe based on some conditions over the rows of dataframe that contains list in r

Tag : r , By : Nic Doye
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I have a dataframe that contains a list of values in each rows of dataframe. I want to check some conditions and based on that condition i want to subset a dataframe. For example in belove code myDF is my dataframe. That is , is that what you want?
myDF$Value_filtered <- lapply(seq(myDF$Value), function(i) myDF$Value[[i]][which(myDF$Value[[i]] %in% vec)])

Combine 1-row dataframe and n-rows dataframe, by duplicating the 1-row and append to n-rows dataframe

Tag : python , By : francisco santos
Date : March 29 2020, 07:55 AM
I wish this help you Use cross join with merge + assign + drop - this solution can be used if want combine multiple rows in d1:
df = pd.merge(d1.assign(A=1), d2.assign(A=1), on='A').drop('A', 1)
print (df)
    Name  Age  Subject Grade
0  Alice   18  Science     A
1  Alice   18     Math     C
2  Alice   18  English     B
df = pd.concat([d1.reindex(d2.index, method='ffill'), d2], axis=1)
print (df)
    Name  Age  Subject Grade
0  Alice   18  Science     A
1  Alice   18     Math     C
2  Alice   18  English     B

Pandas DataFrame: programmatic rows split of a dataframe on multiple columns conditions

Tag : python , By : Rob
Date : October 18 2020, 08:10 AM
With these it helps After several attempts, I managed to achieve my goal.
Here is the code:
import Pandas
import numpy
# assume dataframe exists
df = ...
# initiliaze an array of False, matching df number of rows
resulting_bools = numpy.zeros((1, len(df.index)), dtype=bool)

for col in list_cols:
    # obtain array of booleans for given column and boolean condition for [row, column] value
    criterion = df[col].map(lambda x: x < 0) # same condition for each column, different conditions would have been more difficult (for me)

     # perform cumulative boolean evaluation accross columns
    resulting_bools |= criterion

# use the array of booleans to build the required df
negative_values_matches = df[ resulting_bools].copy() # use .copy() to avoid further possible warnings from Pandas depending on what you do with your data frame
positive_values_matches = df[~resulting_bools].copy()

Delete rows of Dataframe based on multiple conditions from different Dataframe

Tag : python , By : unadopted
Date : March 29 2020, 07:55 AM
With these it helps df1.loc[(df1['date']==dayA)& (df1['location']==placeA)] is the dataframe consisting of rows where the date and location match. drop is expecting the index where they match. So you need df1.loc[(df1['date']==dayA)& (df1['location']==placeA)].index. However, this is a very inefficient method. You can use merge instead as the other answers discuss. Another method would be df1 = df1.loc[~df1[['date','location']].apply(tuple,axis=1).isin(zip(df2.date,df2.location))].

How to extract rows of a pandas dataframe according to conditions based on another dataframe

Tag : python , By : dbarbot
Date : March 29 2020, 07:55 AM
around this issue I have these two dataframes : , Try this:
df1[df1['Points'].isin(df2.query('0 <= Sum <= 2')['Points'])]
  Points  ColX
4       5     5
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