I have the following dataframe:
1 2 3 4 5 6 7 8 9
0 0 0 1 0 0 0 0 0 1
1 0 0 0 0 1 1 0 1 0
2 1 1 0 1 1 0 0 1 1
...
I want to get for each row the longest sequence of value 0 in the row.
so, the expected results for this dataframe will be an array that looks like this:
[5,4,2,...]
as on the first row, maximum sequenc eof value 0 is 5, ect.
I have seen this post and tried for the beginning to get this for the first row (though I would like to do this at once for the whole dataframe) but I got errors:
s=df_day.iloc[0]
(~s).cumsum()[s].value_counts().max()
TypeError: ufunc 'invert' not supported for the input types, and the
inputs could not be safely coerced to any supported types according to
the casting rule ''safe''
when I inserted manually the values like this:
s=pd.Series([0,0,1,0,0,0,0,0,1])
(~s).cumsum()[s].value_counts().max()
>>>7
I got 7 which is number of total 0 in the row but not the max sequence.
However, I don't understand why it raises the error at first, and , more important, I would like to run it on the end on the while dataframe and per row.
My end goal: get the maximum uninterrupted occurance of value 0 in a row.
Vectorized solution for counts consecutive 0 per rows, so for maximal use max of DataFrame c:
#more explain https://stackoverflow.com/a/52718619/2901002
m = df.eq(0)
b = m.cumsum(axis=1)
c = b.sub(b.mask(m).ffill(axis=1).fillna(0)).astype(int)
print (c)
1 2 3 4 5 6 7 8 9
0 1 2 0 1 2 3 4 5 0
1 1 2 3 4 0 0 1 0 1
2 0 0 1 0 0 1 2 0 0
df['max_consecutive_0'] = c.max(axis=1)
print (df)
1 2 3 4 5 6 7 8 9 max_consecutive_0
0 0 0 1 0 0 0 0 0 1 5
1 0 0 0 0 1 1 0 1 0 4
2 1 1 0 1 1 0 0 1 1 2
Use:
df = df.T.apply(lambda x: (x != x.shift()).astype(int).cumsum().where(x.eq(0)).dropna().value_counts().max())
OUTPUT
0 5
1 4
2 2
The following code should do the job.
the function longest_streak will count the number of consecutive zeros and return the max, and you can use apply on your df.
from itertools import groupby
def longest_streak(l):
lst = []
for n,c in groupby(l):
num,count = n,sum(1 for i in c)
if num==0:
lst.append((num,count))
maxx = max([y for x,y in lst])
return(maxx)
df.apply(lambda x: longest_streak(x),axis=1)
My feature engineering runs for different documents. For some documents some features do not exist and followingly the sublist consists only of the same values such as the third sublist [0,0,0,0,0]. One hot encoding of this sublist leads to only one column, while the feature lists of other documents are transformed to two columns. Is there any possibility to tell ohe also to create two columns if it consits only of one and the same value and insert the column in the right spot? The main problem is that my feature dataframe of different documents consists in the end of a different number of columns, which make them not comparable.
import pandas as pd
feature = [[0,0,1,0,0], [1,1,1,0,1], [0,0,0,0,0], [1,0,1,1,1], [1,1,0,1,1], [1,0,1,1,1], [0,1,0,0,0]]
df = pd.DataFrame(feature[0])
df_features_final = pd.get_dummies(df[0])
for feature in feature[1:]:
df = pd.DataFrame(feature)
df_enc = pd.get_dummies(df[0])
print(df_enc)
df_features_final = pd.concat([df_features_final, df_enc], axis = 1, join ='inner')
print(df_features_final)
The result is the following dataframe. As you can see in the changing columntitles, after column 5 does not follow a 1:
0 1 0 1 0 0 1 0 1 0 1 0 1
0 1 0 0 1 1 0 1 0 1 0 1 1 0
1 1 0 0 1 1 1 0 0 1 1 0 0 1
2 0 1 0 1 1 0 1 1 0 0 1 1 0
3 1 0 1 0 1 0 1 0 1 0 1 1 0
4 1 0 0 1 1 0 1 0 1 0 1 1 0
I don't notice the functionality you want in pandas atleast. But, in TensorFlow, we do have
tf.one_hot(
indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None
)
Set depth to 2.
I have a Pandas script that counts the number of readmissions to hospital within 30 days based on a few conditions. I wonder if it could be vectorized to improve performance. I've experimented with df.rolling().apply, but so far without luck.
Here's a table with contrived data to illustrate:
ID VISIT_NO ARRIVED LEFT HAD_A_MASSAGE BROUGHT_A_FRIEND
1 1 29/02/1996 01/03/1996 0 1
1 2 01/12/1996 04/12/1996 1 0
2 1 20/09/1996 21/09/1996 1 0
3 1 27/06/1996 28/06/1996 1 0
3 2 04/07/1996 06/07/1996 0 1
3 3 16/07/1996 18/07/1996 0 1
4 1 21/02/1996 23/02/1996 0 1
4 2 29/04/1996 30/04/1996 1 0
4 3 02/05/1996 02/05/1996 0 1
4 4 02/05/1996 03/05/1996 0 1
5 1 03/10/1996 05/10/1996 1 0
5 2 07/10/1996 08/10/1996 0 1
5 3 10/10/1996 11/10/1996 0 1
First, I create a dictionary with IDs:
ids = massage_df[massage_df['HAD_A_MASSAGE'] == 1]['ID']
id_dict = {id:0 for id in ids}
Everybody in this table has had a massage, but in my real dataset, not all people are so lucky.
Next, I run this bit of code:
for grp, df in massage_df.groupby(['ID']):
date_from = df.loc[df[df['HAD_A_MASSAGE']==1].index, 'LEFT']
date_to = date_from + DateOffset(days=30)
mask = ((date_from.values[0] < df['ARRIVED']) &
(df['ARRIVED'] <= date_to.values[0]) &
(df['BROGHT_A_FRIEND'] == 1))
if len(df[mask]) > 0:
id_dict[df['ID'].iloc[0]] = len(df[mask])
Basically, I want to count the number of times when someone originally came in for a massage (single or with a friend) and then came back within 30 days with a friend. The expected results for this table would be a total of 6 readmissions for IDs 3, 4 and 5.
I have a df with badminton scores. Each sets of a games for a team are on rows and the score at each point on the columns like so:
0 0 1 1 2 3 4
0 1 2 3 3 4 4
I want to obtain only O and 1 when a point is scored, like so: (to analyse if there any pattern in the points):
0 0 1 0 1 1 1
0 1 1 1 0 1 0
I was thinking of using df.itertuples() and iloc and conditions to attribute 1 to new dataframe if next score = score+1 or 0 if next score = score + 1
But I dont know how to iterate through the generated tuples and how to generate my new df with the 0 and 1 at the good locations.
Hope that is clear thanks for your help.
Oh also, any suggestions to analyse the patterns after that ?
You just need diff(If you need convert it back try cumsum)
df.diff(axis=1).fillna(0).astype(int)
Out[1382]:
1 2 3 4 5 6 7
0 0 0 1 0 1 1 1
1 0 1 1 1 0 1 0
I know how to append a column counting the number of elements in a group, but I need to do so just for the number within that group that meets a certain condition.
For example, if I have the following data:
import numpy as np
import pandas as pd
columns=['group1', 'value1']
data = np.array([np.arange(5)]*2).T
mydf = pd.DataFrame(data, columns=columns)
mydf.group1 = [0,0,1,1,2]
mydf.value1 = ['P','F',100,10,0]
valueslist={'50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66','67','68','69','70','71','72','73','74','75','76','77','78','79','80','81','82','83','84','85','86','87','88','89','90','91','92','93','94','95','96','97','98','99','100','A','B','C','D','P','S'}
and my dataframe therefore looks like this:
mydf
group1 value1
0 0 P
1 0 F
2 1 100
3 1 10
4 2 0
I would then want to count the number of rows within each group1 value where value1 is in valuelist.
My desired output is:
group1 value1 count
0 0 P 1
1 0 F 1
2 1 100 1
3 1 10 1
4 2 0 0
After changing the type of the value1 column to match your valueslist (or the other way around), you can use isin to get a True/False column, and convert that to 1s and 0s with astype(int). Then we can apply an ordinary groupby transform:
In [13]: mydf["value1"] = mydf["value1"].astype(str)
In [14]: mydf["count"] = (mydf["value1"].isin(valueslist).astype(int)
.groupby(mydf["group1"]).transform(sum))
In [15]: mydf
Out[15]:
group1 value1 count
0 0 P 1
1 0 F 1
2 1 100 1
3 1 10 1
4 2 0 0
mydf.value1=mydf.value1.astype(str)
mydf['count']=mydf.group1.map(mydf.groupby('group1').apply(lambda x : sum(x.value1.isin(valueslist))))
mydf
Out[412]:
group1 value1 count
0 0 P 1
1 0 F 1
2 1 100 1
3 1 10 1
4 2 0 0
Data input :
valueslist=['50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66','67','68','69','70','71','72','73','74','75','76','77','78','79','80','81','82','83','84','85','86','87','88','89','90','91','92','93','94','95','96','97','98','99','100','A','B','C','D','P','S']
You can groupby each group1 and then use transform to find the max of whether your values are in the list.
mydf['count'] = mydf.groupby('group1').transform(lambda x: x.astype(str).isin(valueslist).sum())
group1 value1 count
0 0 P 1
1 0 F 1
2 1 100 1
3 1 10 1
4 2 0 0
Here is one way to do it, albeit a one-liner:
mydf.merge(mydf.groupby('group1').apply(lambda x: len(set(x['value1'].values).intersection(valueslist))).reset_index().rename(columns={0: 'count'}), how='inner', on='group1')
group1 value1 count
0 0 P 1
1 0 F 1
2 1 100 1
3 1 10 1
4 2 0 0