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Efficient way to apply multiple Boolean mask to set values in a column using pandas


Efficient way to apply multiple Boolean mask to set values in a column using pandas

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Efficient way to apply multiple Boolean mask to set values in a column using pandas
Tag : python , By : RinKaMan
Date : November 25 2020, 03:01 PM

will be helpful for those in need Use numpy.select:
df["membership"] = np.select([mask1, mask2, mask3], [0,1,2], default=3)
df["membership1"] = np.select(list(maskDict.values()), list(maskDict.keys()), default=3)

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How to make assignment to hierarchical column of pandas dataframe using boolean mask?


Tag : python , By : shehan
Date : March 29 2020, 07:55 AM
To fix this issue I have a dataframe like this: , you can just use a list to select you column
idx = df[['val1']] > 20

idx
Out[39]: 
       val1      
site      a     b
time             
1     False  True
2     False  True

df[idx] = 50

df
Out[41]: 
     val1     val2     
site    a   b    a    b
time                   
1      11  50  101  201
2      12  50  102  202

pandas: Calculate new values in the new column from multiple criteria apply from multiple columns without looping


Tag : python , By : Jim Davis
Date : March 29 2020, 07:55 AM
wish helps you I have my data in the following dataframe , You can use the following:
Deal with the Status Closed first:
df.loc[df.Status == 'Closed','ActiveYears'] = df.loc[df.Status == 'Closed','Years']
df.loc[df.Status == 'Active', 'ActiveYears'] = df[df.Status == 'Active'].groupby('AccID')['Years'].transform(max)

print(df)

  AccID AccTypes  Status  Years  ActiveYears
0   001        A  Closed      5          5.0
1   001        B  Active     15         15.0
2   001        C  Active     10         15.0
3   002        A  Active     20         20.0
4   002        B  Closed     25         25.0
5   003        C  Active     30         30.0

Pandas dataframe boolean mask on multiple columns


Tag : python , By : Yolanda N. Ceron
Date : March 29 2020, 07:55 AM
hop of those help? You can use the results of your apply statement to boolean index select from the original dataframe:
results = df[["A","B"]].apply(lambda x: x.abs()-5*df['d'+x.name] > 0)
       A      B
0  False   True
1   True   True
2   True   True
3   True  False
df[results.A]

   A    B     dA     dB
1  2 -4.0  0.263  0.357
2  5  5.0  0.382  0.397
3 -4 -0.5  0.330  0.115
df[results.any(axis=1)]

   A    B     dA     dB
0 -1  3.0  0.310  0.080
1  2 -4.0  0.263  0.357
2  5  5.0  0.382  0.397
3 -4 -0.5  0.330  0.115
df[results.all(axis=1)]

   A    B     dA     dB
1  2 -4.0  0.263  0.357
2  5  5.0  0.382  0.397

Pandas - on each column apply a function returning multiple values


Tag : python , By : glisignoli
Date : March 29 2020, 07:55 AM
I hope this helps you . The key issue is that your function is returning a tuple object, and what res_1, res_2 = my_fun(df['a']) is doing is unpacking the returned tuples res_1 and res_2 as Series object.
To illustrate:
df.apply(my_fun)

# a       ([11, 12, 13], [3, 6, 9])
# b    ([14, 15, 16], [12, 15, 18])
# c    ([20, 21, 22], [30, 33, 36])
# dtype: object

df.applymap(my_fun)

#          a         b         c
# 0  (11, 3)  (14, 12)  (20, 30)
# 1  (12, 6)  (15, 15)  (21, 33)
# 2  (13, 9)  (16, 18)  (22, 36)
df1 = df.apply(my_fun, axis = 0).apply(lambda x: x[0]).transpose()
df2 = df.apply(my_fun, axis = 0).apply(lambda x: x[1]).transpose()

df1

#        a   b   c
#    0  11  14  20
#    1  12  15  21
#    2  13  16  22

df2

#       a   b   c
#    0  3  12  30
#    1  6  15  33
#    2  9  18  36

Setting dataframe by using both iloc and a boolean mask (mask at multiple different index (row) values in the dataframe)


Tag : python , By : Mikael
Date : March 29 2020, 07:55 AM
may help you . I believe need DataFrame.iloc and DataFrame.mask, which set values to NaN by boolean mask by default (only necessary same number of rows and columns of selected df with boolean mask).
Also df2_null mask is converted to numpy array for avoid alignment by indices.
df.iloc[20:24] = df.iloc[20:24].mask(df2_null.values)
print (df.iloc[15:30])
       A     B
15  15.0  15.0
16  16.0  16.0
17  17.0  17.0
18  18.0  18.0
19  19.0  19.0
20  20.0   NaN
21   NaN  21.0
22  22.0   NaN
23  23.0  23.0
24  24.0  24.0
25  25.0  25.0
26  26.0  26.0
27  27.0  27.0
28  28.0  28.0
29  29.0  29.0
df = pd.DataFrame({'A': list(range(0,30)), 'B': list(range(0,30))})

arr = df.values.astype(float)
arr[20:24] = np.where(df2_null.values, np.nan, arr[20:24])
print (arr)
[[ 0.  0.]
 [ 1.  1.]
 [ 2.  2.]
 [ 3.  3.]
 [ 4.  4.]
 [ 5.  5.]
 [ 6.  6.]
 [ 7.  7.]
 [ 8.  8.]
 [ 9.  9.]
 [10. 10.]
 [11. 11.]
 [12. 12.]
 [13. 13.]
 [14. 14.]
 [15. 15.]
 [16. 16.]
 [17. 17.]
 [18. 18.]
 [19. 19.]
 [20. nan]
 [nan 21.]
 [22. nan]
 [23. 23.]
 [24. 24.]
 [25. 25.]
 [26. 26.]
 [27. 27.]
 [28. 28.]
 [29. 29.]]
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