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How to view/average a groupby dataframe when the data is a string?


How to view/average a groupby dataframe when the data is a string?

By : user3042431
Date : November 28 2020, 12:01 PM
it fixes the issue Add a pair of parenthesis at the end of the line to call the mean method:
code :
x = df1.assign(arrival_price=df['arrival_price'].astype(float)).groupby('country', as_index = False)['arrival_price'].mean()


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Average of a groupby or a groupby of an average on a dataframe with duplicated indexes

Average of a groupby or a groupby of an average on a dataframe with duplicated indexes


By : Naveen Kumar Veliget
Date : March 29 2020, 07:55 AM
like below fixes the issue I think the confusion is because you are using a different syntax in the two cases and it's less obvious what the difference is. You're using lambda to operate on the index in the one case:
code :
dd.groupby(lambda x: x.month).mean()
dd.groupby( dd.index.month ).mean()
dd.groupby( dd.index ).mean()
dd.index.date

array([datetime.date(2007, 1, 1), datetime.date(2007, 1, 1),
       datetime.date(2007, 2, 1), datetime.date(2007, 2, 1),
       datetime.date(2008, 1, 1), datetime.date(2008, 1, 1),
       datetime.date(2008, 2, 1), datetime.date(2008, 2, 1)], dtype=object)

dd.index.month
array([1, 1, 2, 2, 1, 1, 2, 2])
dd.groupby([dd.index.year,dd.index.month]).mean()

           value  identifier
2007 1  0.395167        55.5
     2  0.961007        55.5
2008 1  0.639888        55.5
     2  0.220279        55.5
groupby weighted average and sum in pandas dataframe

groupby weighted average and sum in pandas dataframe


By : chirayu sharma
Date : March 29 2020, 07:55 AM
may help you . To pass multiple functions to a groupby object, you need to pass a dictionary with the aggregation functions corresponding to the columns:
code :
# Define a lambda function to compute the weighted mean:
wm = lambda x: np.average(x, weights=df.loc[x.index, "adjusted_lots"])

# Define a dictionary with the functions to apply for a given column:
f = {'adjusted_lots': ['sum'], 'price': {'weighted_mean' : wm} }

# Groupby and aggregate with your dictionary:
df.groupby(["contract", "month", "year", "buys"]).agg(f)

                         adjusted_lots         price
                                   sum weighted_mean
contract month year buys                            
C        Z     5    Sell           -19    424.828947
CC       U     5    Buy              5   3328.000000
SB       V     5    Buy             12     11.637500
W        Z     5    Sell            -5    554.850000
Average of Groupby into a dataframe timedelta64[ns]

Average of Groupby into a dataframe timedelta64[ns]


By : 杜圣哲
Date : March 29 2020, 07:55 AM
hop of those help? df1
code :
import pandas as pd
df1 = pd.DataFrame(
    {'Days': ['20 days', '10 days', '10 days', '5 days', '7 days', '8 days'],
     'Project': ['A', 'B', 'A', 'C', 'C', 'B']}) 

df2 = pd.DataFrame(
    {'Date': ['1/10/16', '1/8/16', '1/2/16', '1/9/16'],
     'Project': ['A', 'A', 'C', 'B']})

df1['Days'] = pd.to_timedelta(df1['Days']) 
df2['Date'] = pd.to_datetime(df2['Date'])

result = df1.groupby('Project')['Days'].agg(['sum', 'count'])
result['Days'] = result['sum']/result['count']
df2 = pd.merge(df2, result[['Days']], left_on='Project', right_index=True)
df2['New Date'] = df2['Date'] + df2['Days']
print(df2)
        Date Project  Days   New Date
0 2016-01-10       A  15.0 2016-01-25
1 2016-01-08       A  15.0 2016-01-23
2 2016-01-02       C   6.0 2016-01-08
3 2016-01-09       B   9.0 2016-01-18
result = df1.groupby('Project')['Days'].agg(['sum', 'count'])
result['Days'] = result['sum']/result['count']
#             sum  count    Days
# Project                       
# A       30 days      2 15 days
# B       18 days      2  9 days
# C       12 days      2  6 days
df2 = pd.merge(df2, result[['Days']], left_on='Project', right_index=True)
#         Date Project    Days
# 0 2016-01-10       A 15 days
# 1 2016-01-08       A 15 days
# 2 2016-01-02       C  6 days
# 3 2016-01-09       B  9 days
df2['New Date'] = df2['Date'] + df2['Days']
Pandas DataFrame Groupby two columns and add column for moving average

Pandas DataFrame Groupby two columns and add column for moving average


By : Sandeep Pidshetti
Date : March 29 2020, 07:55 AM
will be helpful for those in need How about you change your initial groupby to keep the column name 'total'.
code :
df3 = df.groupby(['Station','Train','month_code']).sum()

>>> df3.head()
                          id  total
Station Train month_code           
A       BLUE  1            2    112
              2            6    114
        GREEN 1            3     99
              2            7    100
        RED   1            1    100
df3['average'] = df3['total'].rolling(2).mean()

>>> df3.head()
                          id  total  average
Station Train month_code                    
A       BLUE  1            2    112      NaN
              2            6    114    113.0
        GREEN 1            3     99    106.5
              2            7    100     99.5
        RED   1            1    100    100.0
Return groupby weighted average for multiple pandas dataframe columns as a dataframe

Return groupby weighted average for multiple pandas dataframe columns as a dataframe


By : A P
Date : March 29 2020, 07:55 AM
like below fixes the issue My question is related to this one. However, the solution there isn't working for me. , You can using the trick of reindex and repeat
code :
df.reindex(df.index.repeat(df.counts)).drop('counts',1).\
     groupby(['building','day'],as_index=False).mean()
Out[110]: 
  building         day  elevation  width
0       A1  2019-07-02       7.10   2.00
1       A1  2019-07-03       7.44   2.91
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