I am collecting heart rate values over the course of time. Each subject varies in the length of time that data was collected. I would like to make a table of the last 2 seconds of collected data.
import pandas as pd
import numpy as np
#example data
example_s = [["4/20/21 4:20", 302, 0, 0, 1, 2, 3],
["2/17/21 9:20",135, 1, 1.4, 8, 10, np.NaN, np.NaN],
["2/17/21 9:20", 111, 5, 5,1, np.NaN, np.NaN,np.NaN, np.NaN]]
example_s_table = pd.DataFrame(example_s,columns=['Date_Time','CID', 0, 1, 2, 3, 4, 5, 6])
desired_outcome = [["4/20/21 4:20",302,1, 2, 3],
["2/17/21 9:20",135, 1.4, 8, 10 ],
["2/17/21 9:20",111, 5, 5,1 ]]
desired_outcome_table = pd.DataFrame(desired_outcome,columns=['Date_Time','CID', "Second 1", "Second 2", "Second 3"])
I can see how to collect a single instance of the data from the example shown here, but would like to know how to quickly add multiple values to my table:
desired_outcome_table["Last Second"]=example_s_table.iloc[:,1:].ffill(axis=1).iloc[:, -1]
Python Dataframe Get Value of Last Non Null Column for Each Row
Try:
df = example_s_table.copy()
df = df.set_index(['Date_Time', 'CID'])
df_out = df.mask(df.eq(0))\
.apply(lambda x: pd.Series(x.dropna().tail(3).values), axis=1)\
.rename(columns = lambda x: f'Second {x+1}')
df_out['Last Second'] = df_out['Second 3']
print(df_out.reset_index())
Output:
Date_Time CID Second 1 Second 2 Second 3 Last Second
0 4/20/21 4:20 302 1.0 2.0 3.0 3.0
1 2/17/21 9:20 135 1.4 8.0 10.0 10.0
2 2/17/21 9:20 111 5.0 5.0 1.0 1.0
Related
I need help vectorizing this for loop.
i couldn't come up with my own solution.
So the general idea is that I want to calculate the number of bars since the last time the condition was true.
I have DataFrame with initial values 0 and 1 where 0 is the anchor point for counting to start and stop (0 means that the condition was met for the index in this cell).
For example inital DataFrame would look like this (I am typing only the series raw values and I am ommiting column names etc.)
NaN, NaN, NaN, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0
The output should look like this:
NaN, NaN, NaN, 0, 1, 2, 3, 4, 0, 1, 2, 0, 1, 2, 3, 4, 5, 0
My current code:
cond_count = pd.DataFrame(index=range(cond.shape[0]), columns=range(1))
cond_count.rename(columns={0: 'Bars Since'})
cond_count['Bars Since'] = 'NaN'
cond_count['Bars Since'].iloc[indices_cond_met] = 0
cond_count['Bars Since'].iloc[indices_cond_cut] = 1
for i in range(cond_count.shape[0]):
if cond_count['Bars Since'].iloc[i] == 'NaN'
pass
elif cond_count['Bars Since'].iloc[i] == 0:
while cond_count['Bars Since'].iloc[j] != 0:
cond_count['Bars Since'].iloc[j] = cond_count['Bars Since'].shift(1).iloc[j] + 1
else:
pass
import numpy as np
import pandas as pd
df = pd.DataFrame({'data': [np.nan, np.nan, np.nan, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0]})
df['cs'] = df['data'].le(0).cumsum()
aaa = df.groupby(['data', 'cs'])['data'].apply(lambda x: x.cumsum())
df.loc[aaa.index[0]:, 'data'] = aaa
df = df.drop(['cs'], axis=1)#if you need to remove the auxiliary column
print(df)
Output
data
0 NaN
1 NaN
2 NaN
3 0.0
4 1.0
5 2.0
6 3.0
7 4.0
8 0.0
9 1.0
10 2.0
11 0.0
12 1.0
13 2.0
14 3.0
15 4.0
16 5.0
17 0.0
Here used le to get True where 0.
Then I applied cumsum(), thereby marking the lines into groups.
In the list 'aaa' applied the grouping to the columns 'data', 'cs' and submitted the data to apply, where it applied cumsum().
Using the first slice index: df.loc[aaa.index[0]:, 'data'] in loc overwrote the rows.
I am trying to calculate a sum for each date field, however I only want to calculate the sum of IDs that are in both the current and next date, so a rolling comparison of IDs and then a groupby sum. Currently I have to loop over the dataframe which is very slow.
For example my df:
df = pd.DataFrame({
'Date': [1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4],
'ID': [ 1, 2, 3, 4 , 2, 3, 4 , 2, 3, 4, 5, 1, 2, 3, 4],
'Value': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
})
Ideally I want to group the dataframe by Date and only sum the IDs that are common between two dates, for example below. However this is very slow.
tmpL = df.groupby('Date')['ID'].apply(list)
tmpV = df.groupby('Date')['Value'].sum()
for i in range(1, tmpL.shape[0]):
res = list(set(tmpL.iloc[i]) - set(tmpL.iloc[i - 1]))
v = df.loc[ df.ID.isin(res) & (df.Date == tmpL.index[i]), 'Value'].sum()
tmpV.iloc[i] = tmpV.iloc[i] - v
tmpV
Date
1 10
2 18
3 27
4 42
Name: Value, dtype: int64
Is there a way to do this in pandas without looping over the dataframe?
Use DataFrame.pivot_table with aggregate sum, compare for not equal with DataFrame.diff, and last passed to DataFrame.mask with sum:
df1 = df.pivot_table(index='Date', columns='ID', values='Value', aggfunc='sum')
s = df1.mask(df1.notna().diff().fillna(False)).sum(axis=1)
print (s)
Date
1 10.0
2 18.0
3 27.0
4 42.0
dtype: float64
First solution, I think slowier:
You can get all not matched sets by convert original to sets, then use Series.diff, Series.explode and get all matched values of original by DataFrame.merge, last aggregate sum and subtract:
tmpL = (df.groupby('Date')['ID'].apply(set)
.diff()
.explode()
.reset_index()
.merge(df)
.groupby('Date')['Value']
.sum())
tmpV = df.groupby('Date')['Value'].sum()
out = tmpV.sub(tmpL, fill_value=0)
print (out)
Date
1 10.0
2 18.0
3 27.0
4 42.0
Try:
df = df.pivot_table(index='Date', columns='ID', values='Value')#.reset_index()
condition = df.notna() & df.notna().shift(1)
condition.iloc[0,:]=True
print(df[condition].sum(axis=1))
Output:
Date
1 10.0
2 18.0
3 27.0
4 42.0
I'm aiming to pass a groupby count of values but only considering rows where Item and Item 2 are different. The following achieves this but drops rows if no values are different. If there are one or more values that are present but are identical between Item and Item 2 then I'm hoping to return 0.
import pandas as pd
df = pd.DataFrame({
'Time' : [1,1,1,1,1,1,1,2,2,2,2,2,2,2,3,4,4,4],
'Item' : ['A','A','A','A','A','A','A','B','B','B','B','B','B','B','A','B','B','B'],
'Item2' : ['B','A','A','A','B','B','B','A','A','B','A','B','B','B','A','B','A','A'],
'Value' : [5, 6, 6, 5, 5, 6, 5, 6, 3, 1, 4, 6, 7, 4, 5, 1, 2, 3],
})
df1 = df[df['Item'] != df['Item2']].groupby(['Time']).size().reset_index(name='count')
Intended Output:
Time count
0 1 4
1 2 3
2 3 0
3 4 2
Edit 2:
df = pd.DataFrame({
'Time' : ['1','1','1','1','1','1','1','2','2','2','2','2','2','2','3','4','4','4'],
'Item' : ['A','A','A','A','A','A','A','B','B','B','B','B','B','B','A','B','B','B'],
'Item2' : ['B','A','A','A','B','B','B','A','A','B','A','B','B','B','A','B','A','A'],
'Value' : [2, 6, 6, 5, 3, 3, 4, 6, 5, 1, 4, 6, 7, 4, 5, 1, 2, 3],
})
df1 = (df.assign(new = df['Item'] != df['Item2'])
.groupby('Time')['new']
.mean()
.reset_index(name='avg')
)
Intended Output:
Time avg
0 1 3.0
1 2 5.0
2 3 0.0
3 4 2.5
Idea is not filter, bur count values of Trues per groups by sum, here is passed Series df['Time'] to groupby:
df1 = (df['Item'] != df['Item2']).groupby(df['Time']).sum().reset_index(name='count')
print (df1)
Time count
0 1 4
1 2 3
2 3 0
3 4 2
Another similar solution is create new helper column and aggregate it:
df1 = (df.assign(new = df['Item'] != df['Item2'])
.groupby('Time')['new']
.sum()
.reset_index(name='count'))
EDIT: You can replace non matched values to misisng values by Series.where and then replace misisng values by fillna
df1 = (df.assign(new = df['Value'].where(df['Item'] != df['Item2']))
.groupby('Time')['new']
.mean()
.fillna(0)
.reset_index(name='avg')
)
print (df1)
Time avg
0 1 3.0
1 2 5.0
2 3 0.0
3 4 2.5
Alternative is use Series.reindex by uniqu values of original Time column:
df1 = (df[df['Item'] != df['Item2']]
.groupby(['Time'])['Value']
.mean()
.reindex(df['Time'].unique(), fill_value=0)
.reset_index(name='avg'))
Have a look at the pivot tables for pandas
import pandas as pd
import numpy as np
df = pd.DataFrame({
'Time' : [1,1,1,1,1,1,1,2,2,2,2,2,2,2,3],
'Item' : ['A','A','A','A','A','A','A','B','B','B','B','B','B','B','A'],
'Item2' : ['B','A','A','A','B','B','B','A','A','B','A','B','B','B','A'],
'Value' : [5, 6, 6, 5, 5, 6, 5, 6, 3, 1, 4, 6, 7, 4, 5],
})
# this gives you just the ones were there is a differance
df2 = df[df['Item'] != df['Item2']]
# then sum up the numbers for each item
pd.pivot_table(df2,index='Time',aggfunc='count')
This gives you the table
Item Item2 Value
Time
1 4 4 4
2 3 3 3
Let's assume that I have the following data-frame:
df_raw = pd.DataFrame({"id": [102, 102, 103, 103, 103], "val1": [9,2,4,7,6], "val2": [np.nan, 3, np.nan, 4, 5], "val3": [4, np.nan, np.nan, 5, 1], "date": [pd.Timestamp(2002, 1, 1), pd.Timestamp(2002, 3, 3), pd.Timestamp(2003, 4, 4), pd.Timestamp(2003, 8, 9), pd.Timestamp(2005, 2, 3)]})
I want to have access to the rows where the first occurrence of each id is. So these rows would be:
df_first = pd.DataFrame({"id": [102, 103], "val1": [9, 4], "val2": [np.nan, np.nan], "val3": [4, np.nan], "date": [pd.Timestamp(2002, 1, 1), pd.Timestamp(2003, 4, 4)]})
Basically, at the end what I would like to achieve is fill up the NaNs that appear in the first occurrence of each id. So the final data frame might be:
df_processed = pd.DataFrame({"id": [102, 102, 103, 103, 103], "val1": [9,2,4,7,6], "val2": [-1, 3, -1, 4, 5], "val3": [4, np.nan, -1, 5, 1], "date": [pd.Timestamp(2002, 1, 1), pd.Timestamp(2002, 3, 3), pd.Timestamp(2003, 4, 4), pd.Timestamp(2003, 8, 9), pd.Timestamp(2005, 2, 3)]})
An important note is that the rows are already grouped by id and date and sorted in a ascending manner. So they appear exactly as in the provided example.
IIUC using drop_duplicates then concat
df1=df_raw.drop_duplicates('id').fillna(-1)
target=pd.concat([df1,df_raw.loc[~df_raw.index.isin(df1.index)]]).sort_index()
target
date id val1 val2 val3
0 2002-01-01 102 9 -1.0 4.0
1 2002-03-03 102 2 3.0 NaN
2 2003-04-04 103 4 -1.0 -1.0
3 2003-08-09 103 7 4.0 5.0
4 2005-02-03 103 6 5.0 1.0
You can use pd.Series.duplicated with Boolean row indexing:
mask = ~df_raw['id'].duplicated()
val_cols = ['val2', 'val3']
df_raw.loc[mask, val_cols] = df_raw.loc[mask, val_cols].fillna(-1)
print(df_raw)
id val1 val2 val3 date
0 102 9 -1.0 4.0 2002-01-01
1 102 2 3.0 NaN 2002-03-03
2 103 4 -1.0 -1.0 2003-04-04
3 103 7 4.0 5.0 2003-08-09
4 103 6 5.0 1.0 2005-02-03
Question: Does the get_group method work on a DataFrame with a DatetimeIndexResamplerGroupby index? If so, what is the appropriate syntax?
Sample data:
data = [[2, 4, 1, datetime.datetime(2017, 1, 1)],
[2, 4, 2, datetime.datetime(2017, 1, 5)],
[3, 4, 1, datetime.datetime(2017, 1, 7)]]
df1 = pd.DataFrame(data, columns=list('abc') + ['dates'])
gb3 = df1.set_index('dates').groupby('a').resample('D')
DatetimeIndexResamplerGroupby [freq=<Day>, axis=0, closed=left, label=left, convention=e, base=0]
gb3.sum()
a b c
a dates
2 2017-01-01 2.0 4.0 1.0
2017-01-02 NaN NaN NaN
2017-01-03 NaN NaN NaN
2017-01-04 NaN NaN NaN
2017-01-05 2.0 4.0 2.0
3 2017-01-07 3.0 4.0 1.0
The get_group method is working for me on a pandas.core.groupby.DataFrameGroupBy object.
I've tried various approaches, the typical error is TypeError: Cannot convert input [(0, 1)] of type <class 'tuple'> to Timestamp
The below should be what you're looking for (if I understand the question correctly):
import pandas as pd
import datetime
data = [[2, 4, 1, datetime.datetime(2017, 1, 1)],
[2, 4, 2, datetime.datetime(2017, 1, 5)],
[3, 4, 1, datetime.datetime(2017, 1, 7)]]
df1 = pd.DataFrame(data, columns=list('abc') + ['dates'])
gb3 = df1.groupby(['a',pd.Grouper('dates')])
gb3.get_group((2, '2017-01-01'))
Out[14]:
a b c dates
0 2 4 1 2017-01-01
I believe resample/pd.Grouper can be used interchangeably in this case (someone correct me if I'm wrong). Let me know if this works for you.
Yes it does, the following code returns the monthly values sum of the year 2015
df.resample('MS').sum().resample('Y').get_group('2015-12-31')