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Calculate stoppage time between start and stop events with python

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I have a dataframe with number of serial numbers, each serial number has 'START' and 'STOP' events. I want to calculate stoppage time for each serial number for each day, there can be multiple start stop events in a day but i need to consider cumulative stoppage time(STOP-START). How can I do it with python? Added image of glimpse of data.
enter image description here
This is for 1 serial number, but how to write it for all serial numbers
dfout = pd.DataFrame()
dfout['EventType'] = UptimeS['EventType']
dfout['EventStartTime'] = UptimeS['EventDate']
dfout['SerialNumber'] = UptimeS['SerialNumber']
dfout['change'] = np.where(UptimeS['EventType']!=UptimeS['EventType'].shift(),1,0)
dfout = dfout.loc[dfout['change'] !=0 ,:]
dfout['EventEndTime'] = dfout['EventStartTime'].shift(-1)
dfout = dfout.loc[dfout['EventType']=='STOP_Op']
dfout['CommDownTime'] = pd.to_datetime(dfout['EventEndTime']) - pd.to_datetime(dfout['EventStartTime'])
dfout['CommDownTime'] = pd.to_datetime(dfout['CommDownTime'])
dfout = dfout.groupby(['SerialNumber'])['DownTime'].sum()

how to get Unique count from a DataFrame in case of duplicate index [closed]

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I am working on a dataframe. Data in the image
Q. I want the number of shows released per year but if I'm applying count() function, it's giving me 6 instead of 3. Could anyone suggest how do I get the correct value count.

To get unique value of single year, you can use
count = len(df.loc[df['release_year'] == 1945, 'show_id'].unique())
# or
count = df.loc[df['release_year'] == 1945, 'show_id'].nunique()
To summarize unique value of dataframe by year, you can drop_duplicates() on column show_id first.
df.drop_duplicates(subset=['show_id']).groupby('release_year').count()
Or use value_counts() on column after dropping duplicates.
df.drop_duplicates(subset=['show_id'])['release_year'].value_counts()

df['show_id'].nunique().count()
should do the job.

How do I remove squared brackets from data that is saved as a list in a Dataframe in Python [closed]

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The data is like this and it is in a data frame.
PatientId Payor
0 PAT10000 [Cash, Britam]
1 PAT10001 [Madison, Cash]
2 PAT10002 [Cash]
3 PAT10003 [Cash, Madison, Resolution]
4 PAT10004 [CIC Corporate, Cash]
I want to remove the square brackets and filter all patients who used at least a certain mode of payment eg madison then obtain their ID. Please help.

This will generate a list of tuples (id, payor). (df is the dataframe)
payment = 'Madison'
ids = [(id, df.Payor[i][1:-1]) for i, id in enumerate(df.PatientId) if payment in df.Payor[i]]

let's say, your data frame variable initialized as "df" and after removing square brackets, you want to filter all elements containing "Madison" under "Payor" column
df.replace({'[':''}, regex = True)
df.replace({']':''}, regex = True)
filteredDf = df.loc[df['Payor'].str.contains("Madison")]
print(filteredDf)

How to efficiently calculate co-existence of elements in pandas [closed]

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I have a dataframe in which each row show one transaction, items purchased together. Here is how my dataframe looks like
items
['A','B','C']
['A','C]
['C','F']
...
I need to create a dictionary which shows how many times items have been purchased together, something like below
{'A':[('B',1),('C':5)], 'B': [('A':1),('C':6)], ...}
Right now, I have defined a variable freq and then loop through my dataframe and calculate/update the dictionary (freq). but it's taking very long.
What's the efficient way of calculating this without looping through the dataframe?

You can speed this up with sklearn's MultiLabelBinarizer:
from sklearn.preprocessing import MultiLabelBinarizer
Transform your data using:
mlb = MultiLabelBinarizer()
df = pd.DataFrame(mlb.fit_transform(df['items']),
columns=mlb.classes_,
index=df.index)
to get it in the following format:
A B C F
0 1 1 1 0
1 1 0 1 0
2 0 0 1 1
And then getting you can define a trivial function like:
get_num_coexisting = lambda x, y: (df[x] & df[y]).sum()
And use as so:
get_num_coexisting('A', 'C')
>>> 2

Python :Select the rows for the most recent entry from multiple users [closed]

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I have a dataframe df with 3 columns :
df=pd.DataFrame({
'User':['A','A','B','A','C','B','C'],
'Values':['x','y','z','p','q','r','s'],
'Date':[14,11,14,12,13,10,14]
})
I want to create a new dataframe that will contain the rows corresponding to highest values in the 'Date' columns for each user. For example for the above dataframe I want the desired dataframe to be as follows ( its a jpeg image):
Can anyone help me with this problem?

This answer assumes that there is different maximum values per user in Values column:
In [10]: def get_max(group):
...: return group[group.Date == group.Date.max()]
...:
In [12]: df.groupby('User').apply(get_max).reset_index(drop=True)
Out[12]:
Date User Values
0 14 A x
1 14 B z
2 14 C s