I'm new in pandas and trying to make aggregation. I converted Dataframe to date format and made indexing change for every day.
model['time_only'] = [time.time() for time in model['date']]
model['date_only'] = [date.date() for date in model['date']]
model['cumsum'] = ((model['date_only'].diff() == datetime.timedelta(days=1))*1).cumsum()
def get_out_of_market_data(data):
df = data.copy()
start_market_time = datetime.time(hour=13,minute=30)
end_market_time = datetime.time(hour=20,minute=0)
df['time_only'] = [time.time() for time in df['date']]
df['date_only'] = [date.date() for date in df['date']]
cond = (start_market_time > df['time_only']) | (df['time_only'] >= end_market_time)
return data[cond]
model['date'] = pd.to_datetime(model['date'])
new = model.drop(columns=['time_only', 'date_only'])
get_out_of_market_data(data=new).head(20)
what i get
0 0 65.5000 65.50 65.5000 65.500 DD 1 125 65.500000 2016-01-04 13:15:00 0
26 26 62.7438 62.96 62.6600 62.956 DD 1639 174595 62.781548 2016-01-04 20:00:00 0
27 27 62.5900 62.79 62.5300 62.747 DD 2113 268680 62.650260 2016-01-04 20:15:00 0
28 28 62.7950 62.80 62.5400 62.590 DD 2652 340801 62.652640 2016-01-04 20:30:00 0
29 29 63.1000 63.12 62.7800 62.800 DD 6284 725952 62.963512 2016-01-04 20:45:00 0
30 30 63.2200 63.22 63.0700 63.080 DD 21 699881 63.070114 2016-01-04 21:00:00 0
31 31 63.2200 63.22 63.2200 63.220 DD 7 1973 63.220000 2016-01-04 22:00:00 0
32 32 63.4000 63.40 63.4000 63.400 DD 2 150 63.400000 2016-01-05 00:30:00 1
33 33 62.3700 62.37 62.3700 62.370 DD 3 350 62.370000 2016-01-05 11:00:00 1
34 34 62.1000 62.37 62.1000 62.370 DD 2 300 62.280000 2016-01-05 11:15:00 1
35 35 62.0800 62.08 62.0800 62.080 DD 1 100 62.080000 2016-01-05 11:45:00 1
the last two columns are the time interval from 20:00 to 13:30 with the indexes of change of each day and the indices of change of the day
I tried to group by the last column the interval from 20:00 one day to 13:00 the next with indexing each interval through the groupbuy
I do not fully understand the method, but for example
new.groupby(pd.Grouper(freq='17hours'))
how to move the indexing to this interval ?
You could try creating a new column to represent the market day it belongs to. If the time is less than 13:30:00, it is yesterday's market day, otherwise it is today's market day. Then you can group by it.The code will be:
def get_market_day(dt):
if dt.time() < datetime.time(13, 30, 0):
return dt.date() - datetime.timedelta(days=1)
else:
return dt.date()
df["market_day"] = df["dt"].map(get_market_day)
df.groupby("market_day").agg(...)
I have a dataframe with different columns (like price, id, product and date) and I need to divide this dataframe into several dataframes based on the current date of the system (current_date = np.datetime64(date.today())).
For example, if today is 2020-02-07 I want to divide my main dataframe into three different ones where df1 would be the data of the last month (data of 2020-01-07 to 2020-02-07), df2 would be the data of the last three months (excluding the month already in df1 so it would be more accurate to say from 2019-10-07 to 2020-01-07) and df3 would be the data left on the original dataframe.
Is there some easy way to do this? Also, I've been trying to use Grouper but I keep getting this error over an over again: NameError: name 'Grouper' is not defined (my Pandas version is 0.24.2)
You can use offsets.DateOffset for last 1mont and 3month datetimes, filter by boolean indexing:
rng = pd.date_range('2019-10-10', periods=20, freq='5d')
df = pd.DataFrame({'date': rng, 'id': range(20)})
print (df)
date id
0 2019-10-10 0
1 2019-10-15 1
2 2019-10-20 2
3 2019-10-25 3
4 2019-10-30 4
5 2019-11-04 5
6 2019-11-09 6
7 2019-11-14 7
8 2019-11-19 8
9 2019-11-24 9
10 2019-11-29 10
11 2019-12-04 11
12 2019-12-09 12
13 2019-12-14 13
14 2019-12-19 14
15 2019-12-24 15
16 2019-12-29 16
17 2020-01-03 17
18 2020-01-08 18
19 2020-01-13 19
current_date = pd.to_datetime('now').floor('d')
print (current_date)
2020-02-07 00:00:00
last1m = current_date - pd.DateOffset(months=1)
last3m = current_date - pd.DateOffset(months=3)
m1 = (df['date'] > last1m) & (df['date'] <= current_date)
m2 = (df['date'] > last3m) & (df['date'] <= last1m)
#filter non match m1 or m2 masks
m3 = ~(m1 | m2)
df1 = df[m1]
df2 = df[m2]
df3 = df[m3]
print (df1)
date id
18 2020-01-08 18
19 2020-01-13 19
print (df2)
date id
6 2019-11-09 6
7 2019-11-14 7
8 2019-11-19 8
9 2019-11-24 9
10 2019-11-29 10
11 2019-12-04 11
12 2019-12-09 12
13 2019-12-14 13
14 2019-12-19 14
15 2019-12-24 15
16 2019-12-29 16
17 2020-01-03 17
print (df3)
date id
0 2019-10-10 0
1 2019-10-15 1
2 2019-10-20 2
3 2019-10-25 3
4 2019-10-30 4
5 2019-11-04 5
I have a dataframe where one of the column ('ProcessingDATE') is datetime format. I want to create another column ('Report Date') where if the processing date is a Monday, subtract 3 days from it, which will end to be a Friday; else subtract 1 day from it.
I've been using python for a short amount of time, so doesn't have a lot of idea about how to write it. My thoughts was to write a for loop with if the cell = Monday, then = datetime.datetime.today() – datetime.timedelta(days=3); else = datetime.datetime.today() – datetime.timedelta(days=1)
for j in range(len(DDA_compamy['ProcessingDATE'])):
if pd.to_datetime(datetime(DDA_company.ProcessingDATE[j])).weekday() == 2
Hope this helps,
from datetime import timedelta
if DDA_compamy['ProcessingDATE'].weekday() == 4: #Condition to check if it is friday
DDA_compamy['Report Date']=DDA_compamy['ProcessingDATE'] - timedelta(days=3) # if friday subtracting 3 days
else:
DDA_compamy['Report Date']=DDA_compamy['ProcessingDATE'] - timedelta(days=1) #Else one day from the date is subtracted
the above can also be written as,
DDA_compamy['Report Date'] = (DDA_compamy['ProcessingDATE'] - timedelta(days=3)) if (DDA_compamy['ProcessingDATE'].weekday() == 4) else (DDA_compamy['Report Date']=DDA_compamy['ProcessingDATE'] - timedelta(days=1))
Use pandas.Series.dt.weekday and some logic:
import pandas as pd
df = pd.DataFrame({'ProcessingDATE':pd.date_range('2019-04-01', '2019-04-27')})
df1 = df.copy()
mask = df1['ProcessingDATE'].dt.weekday == 0
df.loc[mask, 'ProcessingDATE'] = df1['ProcessingDATE'] - pd.to_timedelta('3 days')
df.loc[~mask, 'ProcessingDATE'] = df1['ProcessingDATE'] - pd.to_timedelta('1 days')
Output:
ProcessingDATE
0 2019-03-29
1 2019-04-01
2 2019-04-02
3 2019-04-03
4 2019-04-04
5 2019-04-05
6 2019-04-06
7 2019-04-05
8 2019-04-08
9 2019-04-09
10 2019-04-10
11 2019-04-11
12 2019-04-12
13 2019-04-13
14 2019-04-12
15 2019-04-15
16 2019-04-16
17 2019-04-17
18 2019-04-18
19 2019-04-19
20 2019-04-20
21 2019-04-19
22 2019-04-22
23 2019-04-23
24 2019-04-24
25 2019-04-25
26 2019-04-26
I am trying to merge two pandas data frames(DF-1 and DF-2) using a common column (datetime) (I imported both data frames from csv files). I want to add non-common columns from DF-2 into DF-1 ignoring all the common columns from DF-2.
DF-1
date time open high low close datetime col1
2018-01-01 09:15 11 14 17 20 2018-01-01 09:15:00 101
2018-01-01 09:16 12 15 18 21 2018-01-01 09:16:00 102
2018-01-01 09:17 13 16 19 22 2018-01-01 09:17:00 103
DF-2
date time open high low close datetime col2
2018-01-01 09:15 23 26 29 32 2018-01-01 09:15:00 104
2018-01-01 09:16 24 27 30 33 2018-01-01 09:16:00 105
2018-01-01 09:17 25 28 31 34 2018-01-01 09:17:00 106
merged DF(I want)
date time open high low close datetime col1 col2
2018-01-01 09:15 11 14 17 20 2018-01-01 09:15:00 101 104
2018-01-01 09:16 12 15 18 21 2018-01-01 09:16:00 102 105
2018-01-01 09:17 13 16 19 22 2018-01-01 09:17:00 103 106
Code used:
merged_left = pd.merge(left=DF1,right=DF2, how='left', left_on='datetime', right_on='datetime')
What i get:
Is two data framed merged with common columns named
time_x, open_x, high_x, low_x, close_x, time_y, open_y, high_y, low_y, close_y, col1, col2
I want to ignore all _y columns and keep _x
Any help would be greatly appreciated.
You can use suffixes to make sure the second dataframe has it's dupe columns named a certain way. Then you can filter out the columns with filter
>>> df1
a b
0 1 2
>>> df2
a b c
0 1 2 3
>>> df1.merge(df2, on=['a'], suffixes=['', '_y'])
a b b_y c
0 1 2 2 3
>>> df1.merge(df2, on=['a'], how='left', suffixes=['', '_y']).filter(regex='^(?!_y).$', axis=1)
a b c
0 1 2 3
-- Edit --
I find filtering dupe columns this way useful because you can have an arbitrary # of dupes and it'll take them out. You don't have to explicitly pass the columns names you want to keep
You can filter the column within the merge
pd.merge(left=DF1,right=DF2[['datetime','col2']], how='left', left_on='datetime', right_on='datetime')
You could create a list comprehension with all the '_y' columns, then pass that into pandas.drop
drop_labels = [col for col in merged_left.columns if col.find('_y') > 0]
merged_left.drop(drop_labels,axis = 1,inplace = True)
That will leave you with all unique columns and the _x columns
I have some hierarchical data which bottoms out into time series data which looks something like this:
df = pandas.DataFrame(
{'value_a': values_a, 'value_b': values_b},
index=[states, cities, dates])
df.index.names = ['State', 'City', 'Date']
df
value_a value_b
State City Date
Georgia Atlanta 2012-01-01 0 10
2012-01-02 1 11
2012-01-03 2 12
2012-01-04 3 13
Savanna 2012-01-01 4 14
2012-01-02 5 15
2012-01-03 6 16
2012-01-04 7 17
Alabama Mobile 2012-01-01 8 18
2012-01-02 9 19
2012-01-03 10 20
2012-01-04 11 21
Montgomery 2012-01-01 12 22
2012-01-02 13 23
2012-01-03 14 24
2012-01-04 15 25
I'd like to perform time resampling per city, so something like
df.resample("2D", how="sum")
would output
value_a value_b
State City Date
Georgia Atlanta 2012-01-01 1 21
2012-01-03 5 25
Savanna 2012-01-01 9 29
2012-01-03 13 33
Alabama Mobile 2012-01-01 17 37
2012-01-03 21 41
Montgomery 2012-01-01 25 45
2012-01-03 29 49
as is, df.resample('2D', how='sum') gets me
TypeError: Only valid with DatetimeIndex or PeriodIndex
Fair enough, but I'd sort of expect this to work:
>>> df.swaplevel('Date', 'State').resample('2D', how='sum')
TypeError: Only valid with DatetimeIndex or PeriodIndex
at which point I'm really running out of ideas... is there some way stack and unstack might be able to help me?
pd.Grouper
allows you to specify a "groupby instruction for a target object". In
particular, you can use it to group by dates even if df.index is not a DatetimeIndex:
df.groupby(pd.Grouper(freq='2D', level=-1))
The level=-1 tells pd.Grouper to look for the dates in the last level of the MultiIndex.
Moreover, you can use this in conjunction with other level values from the index:
level_values = df.index.get_level_values
result = (df.groupby([level_values(i) for i in [0,1]]
+[pd.Grouper(freq='2D', level=-1)]).sum())
It looks a bit awkward, but using_Grouper turns out to be much faster than my original
suggestion, using_reset_index:
import numpy as np
import pandas as pd
import datetime as DT
def using_Grouper(df):
level_values = df.index.get_level_values
return (df.groupby([level_values(i) for i in [0,1]]
+[pd.Grouper(freq='2D', level=-1)]).sum())
def using_reset_index(df):
df = df.reset_index(level=[0, 1])
return df.groupby(['State','City']).resample('2D').sum()
def using_stack(df):
# http://stackoverflow.com/a/15813787/190597
return (df.unstack(level=[0,1])
.resample('2D').sum()
.stack(level=[2,1])
.swaplevel(2,0))
def make_orig():
values_a = range(16)
values_b = range(10, 26)
states = ['Georgia']*8 + ['Alabama']*8
cities = ['Atlanta']*4 + ['Savanna']*4 + ['Mobile']*4 + ['Montgomery']*4
dates = pd.DatetimeIndex([DT.date(2012,1,1)+DT.timedelta(days = i) for i in range(4)]*4)
df = pd.DataFrame(
{'value_a': values_a, 'value_b': values_b},
index = [states, cities, dates])
df.index.names = ['State', 'City', 'Date']
return df
def make_df(N):
dates = pd.date_range('2000-1-1', periods=N)
states = np.arange(50)
cities = np.arange(10)
index = pd.MultiIndex.from_product([states, cities, dates],
names=['State', 'City', 'Date'])
df = pd.DataFrame(np.random.randint(10, size=(len(index),2)), index=index,
columns=['value_a', 'value_b'])
return df
df = make_orig()
print(using_Grouper(df))
yields
value_a value_b
State City Date
Alabama Mobile 2012-01-01 17 37
2012-01-03 21 41
Montgomery 2012-01-01 25 45
2012-01-03 29 49
Georgia Atlanta 2012-01-01 1 21
2012-01-03 5 25
Savanna 2012-01-01 9 29
2012-01-03 13 33
Here is a benchmark comparing using_Grouper, using_reset_index, using_stack on a 5000-row DataFrame:
In [30]: df = make_df(10)
In [34]: len(df)
Out[34]: 5000
In [32]: %timeit using_Grouper(df)
100 loops, best of 3: 6.03 ms per loop
In [33]: %timeit using_stack(df)
10 loops, best of 3: 22.3 ms per loop
In [31]: %timeit using_reset_index(df)
1 loop, best of 3: 659 ms per loop
You need the groupby() method and provide it with a pd.Grouper for each level of your MultiIndex you wish to maintain in the resulting DataFrame. You can then apply an operation of choice.
To resample date or timestamp levels, you need to set the freq argument with the frequency of choice — a similar approach using pd.TimeGrouper() is deprecated in favour of pd.Grouper() with the freq argument set.
This should give you the DataFrame you need:
df.groupby([pd.Grouper(level='State'),
pd.Grouper(level='City'),
pd.Grouper(level='Date', freq='2D')]
).sum()
The Time Series Guide in the pandas documentation describes resample() as:
... a time-based groupby, followed by a reduction method on each of its groups.
Hence, using groupby() should technically be the same operation as using .resample() on a DataFrame with a single index.
The same paragraph points to the cookbook section on resampling for more advanced examples, where the 'Grouping using a MultiIndex' entry is highly relevant for this question. Hope that helps.
An alternative using stack/unstack
df.unstack(level=[0,1]).resample('2D', how='sum').stack(level=[2,1]).swaplevel(2,0)
value_a value_b
State City Date
Georgia Atlanta 2012-01-01 1 21
Alabama Mobile 2012-01-01 17 37
Montgomery 2012-01-01 25 45
Georgia Savanna 2012-01-01 9 29
Atlanta 2012-01-03 5 25
Alabama Mobile 2012-01-03 21 41
Montgomery 2012-01-03 29 49
Georgia Savanna 2012-01-03 13 33
Notes:
No idea about performance comparison
Possible pandas bug - stack(level=[2,1]) worked, but stack(level=[1,2]) failed
This works:
df.groupby(level=[0,1]).apply(lambda x: x.set_index('Date').resample('2D', how='sum'))
value_a value_b
State City Date
Alabama Mobile 2012-01-01 17 37
2012-01-03 21 41
Montgomery 2012-01-01 25 45
2012-01-03 29 49
Georgia Atlanta 2012-01-01 1 21
2012-01-03 5 25
Savanna 2012-01-01 9 29
2012-01-03 13 33
If the Date column is strings, then convert to datetime beforehand:
df['Date'] = pd.to_datetime(df['Date'])
I had the same issue, was breaking my head for a while, but then I read the documentation of the .resample function in the 0.19.2 docs, and I see there's a new kwarg called "level" that you can use to specify a level in a MultiIndex.
Edit: More details in the "What's New" section.
I know this question is a few years old, but I had the same problem and came to a simpler solution that requires 1 line:
>>> import pandas as pd
>>> ts = pd.read_pickle('time_series.pickle')
>>> ts
xxxxxx1 yyyyyyyyyyyyyyyyyyyyyy1 2012-07-01 1
2012-07-02 13
2012-07-03 1
2012-07-04 1
2012-07-05 10
2012-07-06 4
2012-07-07 47
2012-07-08 0
2012-07-09 3
2012-07-10 22
2012-07-11 3
2012-07-12 0
2012-07-13 22
2012-07-14 1
2012-07-15 2
2012-07-16 2
2012-07-17 8
2012-07-18 0
2012-07-19 1
2012-07-20 10
2012-07-21 0
2012-07-22 3
2012-07-23 0
2012-07-24 35
2012-07-25 6
2012-07-26 1
2012-07-27 0
2012-07-28 6
2012-07-29 23
2012-07-30 0
..
xxxxxxN yyyyyyyyyyyyyyyyyyyyyyN 2014-06-02 0
2014-06-03 1
2014-06-04 0
2014-06-05 0
2014-06-06 0
2014-06-07 0
2014-06-08 2
2014-06-09 0
2014-06-10 0
2014-06-11 0
2014-06-12 0
2014-06-13 0
2014-06-14 0
2014-06-15 0
2014-06-16 0
2014-06-17 0
2014-06-18 0
2014-06-19 0
2014-06-20 0
2014-06-21 0
2014-06-22 0
2014-06-23 0
2014-06-24 0
2014-06-25 4
2014-06-26 0
2014-06-27 1
2014-06-28 0
2014-06-29 0
2014-06-30 1
2014-07-01 0
dtype: int64
>>> ts.unstack().T.resample('W', how='sum').T.stack()
xxxxxx1 yyyyyyyyyyyyyyyyyyyyyy1 2012-06-25/2012-07-01 1
2012-07-02/2012-07-08 76
2012-07-09/2012-07-15 53
2012-07-16/2012-07-22 24
2012-07-23/2012-07-29 71
2012-07-30/2012-08-05 38
2012-08-06/2012-08-12 258
2012-08-13/2012-08-19 144
2012-08-20/2012-08-26 184
2012-08-27/2012-09-02 323
2012-09-03/2012-09-09 198
2012-09-10/2012-09-16 348
2012-09-17/2012-09-23 404
2012-09-24/2012-09-30 380
2012-10-01/2012-10-07 367
2012-10-08/2012-10-14 163
2012-10-15/2012-10-21 338
2012-10-22/2012-10-28 252
2012-10-29/2012-11-04 197
2012-11-05/2012-11-11 336
2012-11-12/2012-11-18 234
2012-11-19/2012-11-25 143
2012-11-26/2012-12-02 204
2012-12-03/2012-12-09 296
2012-12-10/2012-12-16 146
2012-12-17/2012-12-23 85
2012-12-24/2012-12-30 198
2012-12-31/2013-01-06 214
2013-01-07/2013-01-13 229
2013-01-14/2013-01-20 192
...
xxxxxxN yyyyyyyyyyyyyyyyyyyyyyN 2013-12-09/2013-12-15 3
2013-12-16/2013-12-22 0
2013-12-23/2013-12-29 0
2013-12-30/2014-01-05 1
2014-01-06/2014-01-12 3
2014-01-13/2014-01-19 6
2014-01-20/2014-01-26 11
2014-01-27/2014-02-02 0
2014-02-03/2014-02-09 1
2014-02-10/2014-02-16 4
2014-02-17/2014-02-23 3
2014-02-24/2014-03-02 1
2014-03-03/2014-03-09 4
2014-03-10/2014-03-16 0
2014-03-17/2014-03-23 0
2014-03-24/2014-03-30 9
2014-03-31/2014-04-06 1
2014-04-07/2014-04-13 1
2014-04-14/2014-04-20 1
2014-04-21/2014-04-27 2
2014-04-28/2014-05-04 8
2014-05-05/2014-05-11 7
2014-05-12/2014-05-18 5
2014-05-19/2014-05-25 2
2014-05-26/2014-06-01 8
2014-06-02/2014-06-08 3
2014-06-09/2014-06-15 0
2014-06-16/2014-06-22 0
2014-06-23/2014-06-29 5
2014-06-30/2014-07-06 1
dtype: int64
ts.unstack().T.resample('W', how='sum').T.stack() is all it took! Very easy and seems quite performant. The pickle I'm reading in is 331M, so this is a pretty beefy data structure; the resampling takes just a couple seconds on my MacBook Pro.
I haven't checked the efficiency of this, but my instinctual way of performing datetime operations on a multi-index was by a kind of manual "split-apply-combine" process using a dictionary comprehension.
Assuming your DataFrame is unindexed. (You can do .reset_index() first), this works as follows:
Group by the non-date columns
Set "Date" as index and resample each chunk
Reassemble using pd.concat
The final code looks like:
pd.concat({g: x.set_index("Date").resample("2D").mean()
for g, x in house.groupby(["State", "City"])})
I have tried this on my own, pretty short and pretty simple too (I will only work with 2 indexes, and you would get the full idea):
Step 1: resample the date but that would give you the date without the other index :
new=df.reset_index('City').groupby('crime', group_keys=False).resample('2d').sum().pad()
That would give you the date and its count
Step 2: get the categorical index in the same order as the the date :
col=df.reset_index('City').groupby('City', group_keys=False).resample('2D').pad()[['City']]
That would give you a new column with the city names and in the same order as the date.
Step 3: merge the dataframes together
new_df=pd.concat([new, col], axis=1)
It's pretty simple, you can make it really shorter tho.