Suppose I have the following contrived example:
ids types values
1 a 10
1 b 11
1 c 12
2 a -10
2 b -11
3 a 100
Is there way to use panda.pivot() to get the following table?
ids a b c
1 10 11 12
2 -10 -11 NaN
3 100 NaN NaN
You could try something like this -
df.pivot(index='ids', columns='types', values='values')
types a b c
ids
1 10.0 11.0 12.0
2 -10.0 -11.0 NaN
3 100.0 NaN NaN
Related
I have the following pandas DataFrame
Id_household Age_Father Age_child
0 1 30 2
1 1 30 4
2 1 30 4
3 1 30 1
4 2 27 4
5 3 40 14
6 3 40 18
and I want to achieve the following result
Age_Father Age_child_1 Age_child_2 Age_child_3 Age_child_4
Id_household
1 30 1 2.0 4.0 4.0
2 27 4 NaN NaN NaN
3 40 14 18.0 NaN NaN
I tried stacking with multi-index renaming, but I am not very happy with it and I am not able to make everything work properly.
Use this:
df_out = df.set_index([df.groupby('Id_household').cumcount()+1,
'Id_household',
'Age_Father']).unstack(0)
df_out.columns = [f'{i}_{j}' for i, j in df_out.columns]
df_out.reset_index()
Output:
Id_household Age_Father Age_child_1 Age_child_2 Age_child_3 Age_child_4
0 1 30 2.0 4.0 4.0 1.0
1 2 27 4.0 NaN NaN NaN
2 3 40 14.0 18.0 NaN NaN
I am trying to get a rolling sum of the past 3 rows for the same ID but lagging this by 1 row. My attempt looked like the below code and i is the column. There has to be a way to do this but this method doesnt seem to work.
for i in df.columns.values:
df.groupby('Id', group_keys=False)[i].rolling(window=3, min_periods=2).mean().shift(1)
id dollars lag
1 6 nan
1 7 nan
1 6 6.5
3 7 nan
3 4 nan
3 4 5.5
3 3 5
5 6 nan
5 5 nan
5 6 5.5
5 12 5.67
5 7 8.3
I am trying to get a rolling sum of the past 3 rows for the same ID but lagging this by 1 row.
You can create the lagged rolling sum by chaining DataFrame.groupby(ID), .shift(1) for the lag 1, .rolling(3) for the window 3, and .sum() for the sum.
Example: Let's say your dataset is:
import pandas as pd
# Reproducible datasets are your friend!
d = pd.DataFrame({'grp':pd.Series(['A']*4 + ['B']*5 + ['C']*6),
'x':pd.Series(range(15))})
print(d)
grp x
A 0
A 1
A 2
A 3
B 4
B 5
B 6
B 7
B 8
C 9
C 10
C 11
C 12
C 13
C 14
I think what you're asking for is this:
d['y'] = d.groupby('grp')['x'].shift(1).rolling(3).sum()
print(d)
grp x y
A 0 NaN
A 1 NaN
A 2 NaN
A 3 3.0
B 4 NaN
B 5 NaN
B 6 NaN
B 7 15.0
B 8 18.0
C 9 NaN
C 10 NaN
C 11 NaN
C 12 30.0
C 13 33.0
C 14 36.0
I have searched the forums in search of a cleaner way to create a new column in a dataframe that is the sum of the row with the previous row- the opposite of the .diff() function which takes the difference.
this is how I'm currently solving the problem
df = pd.DataFrame ({'c':['dd','ee','ff', 'gg', 'hh'], 'd':[1,2,3,4,5]}
df['e']= df['d'].shift(-1)
df['f'] = df['d'] + df['e']
Your ideas are appreciated.
You can use rolling with a window size of 2 and sum:
df['f'] = df['d'].rolling(2).sum().shift(-1)
c d f
0 dd 1 3.0
1 ee 2 5.0
2 ff 3 7.0
3 gg 4 9.0
4 hh 5 NaN
df.cumsum()
Example:
data = {'a':[1,6,3,9,5], 'b':[13,1,2,5,23]}
df = pd.DataFrame(data)
df =
a b
0 1 13
1 6 1
2 3 2
3 9 5
4 5 23
df.diff()
a b
0 NaN NaN
1 5.0 -12.0
2 -3.0 1.0
3 6.0 3.0
4 -4.0 18.0
df.cumsum()
a b
0 1 13
1 7 14
2 10 16
3 19 21
4 24 44
If you cannot use rolling, due to multindex or else, you can try using .cumsum(), and then .diff(-2) to sub the .cumsum() result from two positions before.
data = {'a':[1,6,3,9,5,30, 101, 8]}
df = pd.DataFrame(data)
df['opp_diff'] = df['a'].cumsum().diff(2)
a opp_diff
0 1 NaN
1 6 NaN
2 3 9.0
3 9 12.0
4 5 14.0
5 30 35.0
6 101 131.0
7 8 109.0
Generally to get an inverse of .diff(n) you should be able to do .cumsum().diff(n+1). The issue is that that you will get n+1 first results as NaNs
In my data frame I want to create a column '5D_Peak' as a rolling max, and then another column with rolling count of historical data that's close to the peak. I wonder if there is an easier way to simply or ideally vectorise the calculation.
This is my codes in a plain but complicated way:
import numpy as np
import pandas as pd
df = pd.DataFrame([[1,2,4],[4,5,2],[3,5,8],[1,8,6],[5,2,8],[1,4,10],[3,5,9],[1,4,7],[1,4,6]], columns=list('ABC'))
df['5D_Peak']=df['C'].rolling(window=5,center=False).max()
for i in range(5,len(df.A)):
val=0
for j in range(i-5,i):
if df.loc[j,'C']>df.loc[i,'5D_Peak']-2 and df.loc[j,'C']<df.loc[i,'5D_Peak']+2:
val+=1
df.loc[i,'5D_Close_to_Peak_Count']=val
This is the output I want:
A B C 5D_Peak 5D_Close_to_Peak_Count
0 1 2 4 NaN NaN
1 4 5 2 NaN NaN
2 3 5 8 NaN NaN
3 1 8 6 NaN NaN
4 5 2 8 8.0 NaN
5 1 4 10 10.0 0.0
6 3 5 9 10.0 1.0
7 1 4 7 10.0 2.0
8 1 4 6 10.0 2.0
I believe this is what you want. You can set the two values below:
'''the window within which to search "close-to_peak" values'''
lkp_rng = 5
'''how close is close?'''
closeness_measure = 2
'''function to count the number of "close-to_peak" values in the lkp_rng'''
fc = lambda x: np.count_nonzero(np.where(x >= x.max()- closeness_measure))
'''apply fc to the coulmn you choose'''
df['5D_Close_to_Peak_Count'] = df['C'].rolling(window=lkp_range,center=False).apply(fc)
df.head(10)
A B C 5D_Peak 5D_Close_to_Peak_Count
0 1 2 4 NaN NaN
1 4 5 2 NaN NaN
2 3 5 8 NaN NaN
3 1 8 6 NaN NaN
4 5 2 8 8.0 3.0
5 1 4 10 10.0 3.0
6 3 5 9 10.0 3.0
7 1 4 7 10.0 3.0
8 1 4 6 10.0 2.0
I am guessing what you mean by "historical data".
i have two data frames predictor_df and solution_df like this :
predictor_df
1000 A B C
1001 1 2 3
1002 4 5 6
1003 7 8 9
1004 Nan Nan Nan
and a solution_df
0 D
1 10
2 11
3 12
the reason for the names is that the predictor_df is used to do some analysis on it's columns to arrive at analysis_df . My analysis leaves the rows with Nan values in predictor_df and hence the shorter solution_df
Now i want to know how to join these two dataframes to obtain my final dataframe as
A B C D
1 2 3 10
4 5 6 11
7 8 9 12
Nan Nan Nan
please guide me through it . thanks in advance.
Edit : i tried to merge the two dataframes but the result comes like this ,
A B C D
1 2 3 Nan
4 5 6 Nan
7 8 9 Nan
Nan Nan Nan
Edit 2 : also when i do pd.concat([predictor_df, solution_df], axis = 1)
it becomes like this
A B C D
Nan Nan Nan 10
Nan Nan Nan 11
Nan Nan Nan 12
Nan Nan Nan Nan
You could use reset_index with drop=True which resets the index to the default integer index.
pd.concat([df_1.reset_index(drop=True), df_2.reset_index(drop=True)], axis=1)
A B C D
0 1 2 3 10.0
1 4 5 6 11.0
2 7 8 9 12.0
3 Nan Nan Nan NaN