I have a dataframe with multiple columns. One of these columns consists of boolean numbers. For example:
data = pd.DataFrame([0,0,0,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,1,1,1,1,0,0])
What I need to do is identify every group of 1s and add a constant number, except the first group of 1s.
The output should be a dataframe as follows:
0,0,0,0,1,1,1,0,0,0,0,0,2,2,0,0,0,0,3,3,3,3,0,0
Is there a way to make this without being messy and complicated?
Use a boolean mask:
# Look for current row = 1 and previous row = 0
m = df['A'].diff().eq(1)
df['G'] = m.cumsum().mask(df['A'].eq(0), 0)
print(df)
# Output
A G # m
0 0 0 # False
1 0 0 # False
2 0 0 # False
3 0 0 # False
4 1 1 # True <- Group 1
5 1 1 # False
6 1 1 # False
7 0 0 # False
8 0 0 # False
9 0 0 # False
10 0 0 # False
11 0 0 # False
12 1 2 # True <- Group 2
13 1 2 # False
14 0 0 # False
15 0 0 # False
16 0 0 # False
17 0 0 # False
18 1 3 # True <- Group 3
19 1 3 # False
20 1 3 # False
21 1 3 # False
22 0 0 # False
23 0 0 # False
Related
in this dataframe:
Feat1 Feat2 Feat3 Feat4 Labels
-46.220314 22.862856 -6.1573067 5.6060414 2
-23.80669 20.536781 -5.015675 4.2216353 2
-42.092365 25.680704 -5.0092897 5.665794 2
-35.29639 21.709473 -4.160352 5.578346 2
-37.075096 22.347767 -3.860426 5.6953945 2
-42.8849 28.03802 -7.8572545 3.3361 2
-32.3057 26.568039 -9.47018 3.4532788 2
-24.469942 27.005375 -9.301921 4.3995037 2
-97.89892 -0.38156664 6.4163384 7.234347 1
-81.96325 0.1821717 -1.2870358 4.703838 1
-78.41986 -6.766374 0.8001185 0.83444935 1
-100.68544 -4.5810957 1.6977689 1.8801615 1
-87.05412 -2.9231584 6.817379 5.4460077 1
-64.121056 -3.7892206 -0.283514 6.3084154 1
-94.504845 -0.9999217 3.2884297 6.881124 1
-61.951996 -8.960198 -1.5915259 5.6160254 1
-108.19452 13.909201 0.6966458 -1.956591 0
-97.4037 22.897585 -2.8488266 1.4105041 0
-92.641335 22.10624 -3.5110545 2.467166 0
-199.18787 3.3090565 -2.5994794 4.0802555 0
-137.5976 6.795896 1.6793671 2.2256763 0
-208.0035 -1.33229 -3.2078092 1.5177402 0
-108.225975 14.341716 1.02891 -1.8651972 0
-121.29299 18.274035 2.2891548 2.3360753 0
I wanted to sort the rows based on different column values in the "Labels" column.
I am able to sort in ascending such that the labels appear as [0 1 2] via the command
df2 = df1.sort_values(by = 'Labels', ascending = True)
Then ascending = False, where the labels appear [2 1 0].
How then do I go about sorting the labels as [1 0 2]?
Any help will be greatly appreciated!
Here's a way using Categorical:
df['Labels'] = pd.Categorical(df['Labels'],
categories = [1, 0, 2],
ordered=True)
df.sort_values('Labels')
Output:
Feat1 Feat2 Feat3 Feat4 Labels
11 -100.685440 -4.581096 1.697769 1.880162 1
15 -61.951996 -8.960198 -1.591526 5.616025 1
8 -97.898920 -0.381567 6.416338 7.234347 1
9 -81.963250 0.182172 -1.287036 4.703838 1
10 -78.419860 -6.766374 0.800118 0.834449 1
14 -94.504845 -0.999922 3.288430 6.881124 1
12 -87.054120 -2.923158 6.817379 5.446008 1
13 -64.121056 -3.789221 -0.283514 6.308415 1
21 -208.003500 -1.332290 -3.207809 1.517740 0
20 -137.597600 6.795896 1.679367 2.225676 0
19 -199.187870 3.309057 -2.599479 4.080255 0
18 -92.641335 22.106240 -3.511055 2.467166 0
17 -97.403700 22.897585 -2.848827 1.410504 0
16 -108.194520 13.909201 0.696646 -1.956591 0
23 -121.292990 18.274035 2.289155 2.336075 0
22 -108.225975 14.341716 1.028910 -1.865197 0
7 -24.469942 27.005375 -9.301921 4.399504 2
6 -32.305700 26.568039 -9.470180 3.453279 2
5 -42.884900 28.038020 -7.857254 3.336100 2
4 -37.075096 22.347767 -3.860426 5.695394 2
3 -35.296390 21.709473 -4.160352 5.578346 2
2 -42.092365 25.680704 -5.009290 5.665794 2
1 -23.806690 20.536781 -5.015675 4.221635 2
0 -46.220314 22.862856 -6.157307 5.606041 2
You can use an ordered Categorical, or if you don't want to change the DataFrame, the poor-man's variant, a mapping Series:
order = [1, 0, 2]
key = pd.Series({k:v for v,k in enumerate(order)}).get
# or
# pd.Series(range(len(order)), index=order).get
df1.sort_values(by='Labels', key=key)
Example:
df1 = pd.DataFrame({'Labels': [1,0,1,2,0,2,1]})
order = [1, 0, 2]
key = pd.Series({k:v for v,k in enumerate(order)}).get
print(df1.sort_values(by='Labels', key=key))
Labels
0 1
2 1
6 1
1 0
4 0
3 2
5 2
here is another way to do it
create a new column using map and map the new order sequence and then sort as usual
df['sort_label'] = df['Labels'].map({1:0, 0:1, 2:2 }) #).sort_values('sort_label', ascending=False)
df.sort_values('sort_label')
Feat1 Feat2 Feat3 Feat4 Labels sort_label
11 -100.685440 -4.581096 1.697769 1.880162 1 0
15 -61.951996 -8.960198 -1.591526 5.616025 1 0
8 -97.898920 -0.381567 6.416338 7.234347 1 0
9 -81.963250 0.182172 -1.287036 4.703838 1 0
10 -78.419860 -6.766374 0.800119 0.834449 1 0
14 -94.504845 -0.999922 3.288430 6.881124 1 0
12 -87.054120 -2.923158 6.817379 5.446008 1 0
13 -64.121056 -3.789221 -0.283514 6.308415 1 0
21 -208.003500 -1.332290 -3.207809 1.517740 0 1
20 -137.597600 6.795896 1.679367 2.225676 0 1
19 -199.187870 3.309057 -2.599479 4.080255 0 1
18 -92.641335 22.106240 -3.511054 2.467166 0 1
17 -97.403700 22.897585 -2.848827 1.410504 0 1
16 -108.194520 13.909201 0.696646 -1.956591 0 1
23 -121.292990 18.274035 2.289155 2.336075 0 1
22 -108.225975 14.341716 1.028910 -1.865197 0 1
7 -24.469942 27.005375 -9.301921 4.399504 2 2
6 -32.305700 26.568039 -9.470180 3.453279 2 2
5 -42.884900 28.038020 -7.857254 3.336100 2 2
4 -37.075096 22.347767 -3.860426 5.695394 2 2
3 -35.296390 21.709473 -4.160352 5.578346 2 2
2 -42.092365 25.680704 -5.009290 5.665794 2 2
1 -23.806690 20.536781 -5.015675 4.221635 2 2
0 -46.220314 22.862856 -6.157307 5.606041 2 2
I've got 4 columns with numeric values between 1 and 4, and I'm trying to see which rows change from a value of 1 to a value of 4 progressing from column a to column d within those 4 columns. Currently I'm pulling the difference between each of the columns and looking for a value of 3. Is there a better way to do this?
Here's what I'm looking for (with 0's in place of nan):
ID a b c d check
1 1 0 1 4 True
2 1 0 1 1 False
3 1 1 1 4 True
4 1 3 3 4 True
5 0 0 1 4 True
6 1 2 3 3 False
7 1 0 0 4 True
8 1 4 4 4 True
9 1 4 3 4 True
10 1 4 1 1 True
You can just do cummax
col = ['a','b','c','d']
s = df[col].cummax(1)
df['new'] = s[col[:3]].eq(1).any(1) & s[col[-1]].eq(4)
Out[523]:
0 True
1 False
2 True
3 True
4 True
5 False
6 True
7 True
8 True
dtype: bool
You can try compare the index of 4 and 1 in apply
cols = ['a', 'b', 'c', 'd']
def get_index(lst, num):
return lst.index(num) if num in lst else -1
df['Check'] = df[cols].apply(lambda row: get_index(row.tolist(), 4) > get_index(row.tolist(), 1), axis=1)
print(df)
ID a b c d check Check
0 1 1 0 1 4 True True
1 2 1 0 1 1 False False
2 3 1 1 1 4 True True
3 4 1 3 3 4 True True
4 5 0 0 1 4 True True
5 6 1 2 3 3 False False
6 7 1 0 0 4 True True
7 8 1 4 4 4 True True
8 9 1 4 3 4 True True
I have dataframe with columns A,B and flag. I want to calculate mean of 2 values before flag change from 0 to 1 , and record value when flag change from 0 to 1 and record value when flag changes from 1 to 0.
# Input dataframe
df=pd.DataFrame({'A':[1,3,4,7,8,11,1,15,20,15,16,87],
'B':[1,3,4,6,8,11,1,19,20,15,16,87],
'flag':[0,0,0,0,1,1,1,0,0,0,0,0]})
# Expected output
df_out=df=pd.DataFrame({'A_mean_before_flag_change':[5.5],
'B_mean_before_flag_change':[5],
'A_value_before_change_flag':[7],
'B_value_before_change_flag':[6]})
I try to create more general solution:
df=pd.DataFrame({'A':[1,3,4,7,8,11,1,15,20,15,16,87],
'B':[1,3,4,6,8,11,1,19,20,15,16,87],
'flag':[0,0,0,0,1,1,1,0,0,1,0,1]})
print (df)
A B flag
0 1 1 0
1 3 3 0
2 4 4 0
3 7 6 0
4 8 8 1
5 11 11 1
6 1 1 1
7 15 19 0
8 20 20 0
9 15 15 1
10 16 16 0
11 87 87 1
First create groups by mask for 0 with next 1 values of flag:
m1 = df['flag'].eq(0) & df['flag'].shift(-1).eq(1)
df['g'] = m1.iloc[::-1].cumsum()
print (df)
A B flag g
0 1 1 0 3
1 3 3 0 3
2 4 4 0 3
3 7 6 0 3
4 8 8 1 2
5 11 11 1 2
6 1 1 1 2
7 15 19 0 2
8 20 20 0 2
9 15 15 1 1
10 16 16 0 1
11 87 87 1 0
then filter out groups with size less like N:
N = 4
df1 = df[df['g'].map(df['g'].value_counts()).ge(N)].copy()
print (df1)
A B flag g
0 1 1 0 3
1 3 3 0 3
2 4 4 0 3
3 7 6 0 3
4 8 8 1 2
5 11 11 1 2
6 1 1 1 2
7 15 19 0 2
8 20 20 0 2
Filter last N rows:
df2 = df1.groupby('g').tail(N)
And aggregate last with mean:
d = {'mean':'_mean_before_flag_change', 'last': '_value_before_change_flag'}
df3 = df2.groupby('g')['A','B'].agg(['mean','last']).sort_index(axis=1, level=1).rename(columns=d)
df3.columns = df3.columns.map(''.join)
print (df3)
A_value_before_change_flag B_value_before_change_flag \
g
2 20 20
3 7 6
A_mean_before_flag_change B_mean_before_flag_change
g
2 11.75 12.75
3 3.75 3.50
I'm assuming that this needs to work for cases with more than one rising edge and that the consecutive values and averages get appended to the output lists:
# the first step is to extract the rising and falling edges using diff(), identify sections and length
df['flag_diff'] = df.flag.diff().fillna(0)
df['flag_sections'] = (df.flag_diff != 0).cumsum()
df['flag_sum'] = df.flag.groupby(df.flag_sections).transform('sum')
# then you can get the relevant indices by checking for the rising edges
rising_edges = df.index[df.flag_diff==1.0]
val_indices = [i-1 for i in rising_edges]
avg_indices = [(i-2,i-1) for i in rising_edges]
# and finally iterate over the relevant sections
df_out = pd.DataFrame()
df_out['A_mean_before_flag_change'] = [df.A.loc[tpl[0]:tpl[1]].mean() for tpl in avg_indices]
df_out['B_mean_before_flag_change'] = [df.B.loc[tpl[0]:tpl[1]].mean() for tpl in avg_indices]
df_out['A_value_before_change_flag'] = [df.A.loc[idx] for idx in val_indices]
df_out['B_value_before_change_flag'] = [df.B.loc[idx] for idx in val_indices]
df_out['length'] = [df.flag_sum.loc[idx] for idx in rising_edges]
df_out.index = rising_edges
I have data in excel which have two columns 'Peak Value' & 'Label'. I want to add value in 'Label' column based on 'Peak Value' column.
So, Input looks like below
Peak Value 0 0 0 88 0 0 88 0 0 88 0
Label 0 0 0 0 0 0 0 0 0 0 0
Input
Whenever the value in 'Peak Value' is greater than zero then it add 1 in 'Label' and replace all the zeros below it. For the next value greater than zero it should get incremented to 2 and replace all the zeros by 2.
So, the output will look like this:
Peak Value 0 0 0 88 0 0 88 0 0 88 0
Label 0 0 0 1 1 1 2 2 2 3 3
Output
and so on....
I tried writing function but I am only able to add 1 when the value is greater than 0 in 'Peak Value'.
def funct(row):
if row['Peak Value']>0:
val = 1
else:
val = 0
return val
df['Label']= df.apply(funct, axis=1)
May be you could try using cumsum and ffill:
import numpy as np
df['Labels'] = (df['Peak Value'] > 0).groupby(df['Peak Value']).cumsum()
df['Labels'] = df['Labels'].replace(0, np.nan).ffill().replace(np.nan, 0).astype(int)
Output:
Peak Value Labels
0 0 0
1 0 0
2 0 0
3 88 1
4 0 1
5 0 1
6 88 2
7 0 2
8 0 2
9 88 3
10 0 3
In the below example I only want to retain the row 1 and 2
I want to delete all the rows which has 0 anywhere across the column:
kt b tt mky depth
1 1 1 1 1 4
2 2 2 2 2 2
3 3 3 0 3 3
4 0 4 0 0 0
5 5 5 5 5 0
the output should read like below:
kt b tt mky depth
1 1 1 1 1 4
2 2 2 2 2 2
I have tried:
df.loc[(df!=0).any(axis=1)]
But it deletes the row only if all of its corresponding columns are 0
You are really close, need DataFrame.all for check all Trues per row:
df = df.loc[(df!=0).all(axis=1)]
print (df)
kt b tt mky depth
1 1 1 1 1 4
2 2 2 2 2 2
Details:
print (df!=0)
kt b tt mky depth
1 True True True True True
2 True True True True True
3 True True False True True
4 False True False False False
5 True True True True False
print ((df!=0).all(axis=1))
1 True
2 True
3 False
4 False
5 False
dtype: bool
Alternative solution with any for check at least one True for row with changed mask df == 0 and inversing by ~:
df = df.loc[~(df==0).any(axis=1)]