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Grouping of a dataframe monthly after calculating the highest daily values

I've got a dataframe with two columns one is datetime dataframe consisting of dates, and another one consists of quantity. It looks like something like this,
Date Quantity
0 2019-01-05 10
1 2019-01-10 15
2 2019-01-22 14
3 2019-02-03 12
4 2019-05-11 25
5 2019-05-21 4
6 2019-07-08 1
7 2019-07-30 15
8 2019-09-05 31
9 2019-09-10 44
10 2019-09-25 8
11 2019-12-09 10
12 2020-04-11 111
13 2020-04-17 5
14 2020-06-05 17
15 2020-06-16 12
16 2020-06-22 14
I want to make another dataframe. It should consist of two columns one is Month/Year and the other is Till Highest. I basically want to calculate the highest quantity value until that month and group it using month/year. Example of what I want precisely is,
Month/Year Till Highest
0 Jan/2019 15
1 Feb/2019 15
2 May/2019 25
3 Jul/2019 25
4 Sep/2019 44
5 Dec/2019 44
6 Apr/2020 111
7 Jun/2020 111
In my case, the dataset is vast, and I've readings of almost every day of each month and each year in the specified timeline. Here I've made a dummy dataset to show an example of what I want.
Please help me with this. Thanks in advance :)

See the annotated code:
(df
# convert date to monthly period (2019-01)
.assign(Date=pd.to_datetime(df['Date']).dt.to_period('M'))
# period and max quantity per month
.groupby('Date')
.agg(**{'Month/Year': ('Date', 'first'),
'Till highest': ('Quantity', 'max')})
# format periods as Jan/2019 and get cumulated max quantity
.assign(**{
'Month/Year': lambda d: d['Month/Year'].dt.strftime('%b/%Y'),
'Till highest': lambda d: d['Till highest'].cummax()
})
# drop the groupby index
.reset_index(drop=True)
)
output:
Month/Year Till highest
0 Jan/2019 15
1 Feb/2019 15
2 May/2019 25
3 Jul/2019 25
4 Sep/2019 44
5 Dec/2019 44
6 Apr/2020 111
7 Jun/2020 111

In R you can use cummax:
df=data.frame(Date=c("2019-01-05","2019-01-10","2019-01-22","2019-02-03","2019-05-11","2019-05-21","2019-07-08","2019-07-30","2019-09-05","2019-09-10","2019-09-25","2019-12-09","2020-04-11","2020-04-17","2020-06-05","2020-06-16","2020-06-22"),Quantity=c(10,15,14,12,25,4,1,15,31,44,8,10,111,5,17,12,14))
data.frame(`Month/Year`=unique(format(as.Date(df$Date),"%b/%Y")),
`Till Highest`=cummax(tapply(df$Quantity,sub("-..$","",df$Date),max)),
check.names=F,row.names=NULL)
Month/Year Till Highest
1 Jan/2019 15
2 Feb/2019 15
3 May/2019 25
4 Jul/2019 25
5 Sep/2019 44
6 Dec/2019 44
7 Apr/2020 111
8 Jun/2020 111

Pandas vectorization with two dataframe

assume I have the following two dataframes. DataFrame A and DataFrame B.
DataFrame A has four columns. Year, Month, day and temperature. (e.g. 2021 || 7 || 5 || 23). Currently, some of the temperature cell in DataFrame A are NaN.
DataFrame B has two columns. Date and temperature. (e.g. 2021/7/7 || 28)
The time interval of DataFrame A and DataFrame B are different. The time interval of DataFrame A is smaller than interval B. But some of them overlap. (e.g. every 10 mins in DataFrame B and every 5 mins in DataFrame A).
Now I want to copy the temperature data from DataFrame B to DataFrame A if there is a NaN value in DataFrame A.
I have a method which using looping, but it is very slow. I want to use pandas vectorization. But I don't know how. Can anyone teach me?
for i in tqdm(range(len(dfA['Temp']))):
if(pd.isna(df['Temp'].iloc[i])):
date_time_str = str(year) + '/' + str(month) + '/' + str(day)
try:
dfA['temp'].iloc[i] = float(dfB.loc[dfB['Date'] == date_time_str].iloc[:, 1])
except:
print("no value")
pass
My solution is very slow, how to do it with pandas vectorization?
Method I tried for vectorization:
dfA.loc[df['temp'].isnull() & ((datetime.datetime(dfA['Year'], df['*Month'], dfA['Day']).strftime("%Y/%m/%d %H:%M"))in dfB.Date.values) , 'temp'] = float(dfB[dfB['Date'] == datetime.datetime(dfA['Year'], df['*Month'], dfA['Day']].iloc[:, 1])
Above is my method and trying, it doesn't work.
Example data:
DataFrame A
Year Month Day Temperature
2020 1 17 25
2020 1 18 NaN
2020 1 19 28
2020 1 20 NaN
2020 1 21 NaN
2020 1 22 NaN
DataFrame B
Date Temp
1/17/2020 25
1/19/2020 28
1/21/2020 31
1/23/2020 34
1/25/2020 23
1/27/2020 54
Expected Output
Year Month Day Temperature
2020 1 17 25
2020 1 18 NaN
2020 1 19 28
2020 1 20 NaN
2020 1 21 31
2020 1 22 NaN

Let's map them:
dfa['Date']=pd.to_datetime(dfa[['Day','Month','Year']])
dfb['Date']=pd.to_datetime(dfb['Date'])
dfb['Temperature']=dfa.pop('Date').map(dfb.set_index('Date')['Temp'])
OR
Let's Merge them:
dfa['Date']=pd.to_datetime(dfa[['Day','Month','Year']])
dfb['Date']=pd.to_datetime(dfb['Date'])
dfa=dfa.merge(dfb[['Date','Temp']],on='Date',how='left')
dfa['Temperature']=dfa['Temperature'].fillna(dfa.pop('Temp'))

One way using pandas.to_datetime with pandas.Series.fillna:
df1 = df1.set_index(pd.to_datetime(df1[["Year", "Month", "Day"]]))
s = df2.set_index(pd.to_datetime(df2.pop("Date"))).squeeze()
df1["Temperature"] = df1["Temperature"].fillna(s)
print(df1.reset_index(drop=True))
Output:
Year Month Day Temperature
0 2020 1 17 25.0
1 2020 1 18 NaN
2 2020 1 19 28.0
3 2020 1 20 NaN
4 2020 1 21 31.0
5 2020 1 22 NaN

Python - How to export monthly data into excel based on month?

I have dataframe that contain two columns. Date from 2018 until now and Orders with order count for each day.
Date Orders
0 2018-01-01 57
1 2018-01-02 324
2 2018-01-03 54
3 2018-01-04 677
4 2018-01-05 234
5 2018-01-06 54
6 2018-01-07 234
7 2018-01-08 65
8 2018-01-09 234
9 2018-01-10 54
10 2018-01-11 234
11 2018-01-12 65
12 2018-01-13 7
13 2018-01-14 6
14 2018-01-15 57
15 2018-01-16 324
16 2018-01-17 54
17 2018-01-18 677
18 2018-01-19 234
19 2018-01-20 54
...
I need to export this into multiple excel files so that every files contain only data for one particular month.
I am trying to work on this script but i am struck:
import pandas as pd
df = pd.read_excel("data/SampleData.xlsx")
for dates in Date:
currMonth = something???
filename = 'file_'+list(set(pd.to_datetime(df.loc[currMonth,
'datestart']).dt.strftime('%m%d%y')))[0]+'.xlsx'
df.loc[idx, 'data'].to_excel(filename)
So I think i have to create variable that will store start and end of each month and than iterate through it.
Any idea how to get this to work?

You might want to have a look here. You can use simple integers to address the month, so you should be able to iterate like this (not tested):
for month in range(1, 13):
df_per_month = df[df['Date'].dt.month == month]
df_per_month.to_excel(f'{month}.xlsx')
Edit: Note that according to docs, month ranges from 1-12.
Also, if you want to iterate month and year, you would have to do something like:
for year in range(2018, 2022):
for month in range(1, 13):
data = df[(df['Date'].dt.month == month) & (df['Date'].dt.year == year)]
data.to_excel(f'{month}-{year}.xlsx')

Converting object into time and grouping/summarizing time (H/M/S) into 24 hours

I subsetted a big dataframe, slicing only one column Start Time with `type(object).
test = taxi_2020['Start Time']
Got a column
0 00:15:00
1 00:15:00
2 00:15:00
3 00:15:00
4 00:15:00
...
4137289 00:00:00
4137290 00:00:00
4137291 00:00:00
4137292 00:00:00
4137293 00:00:00
Name: Start Time, Length: 4137294, dtype: object
Then I grouped and summarized it by the count (to my best knowledge)
test.value_counts().sort_index().reset_index()
and got two columns
index Start Time
0 00:00:00 24005
1 00:15:00 22815
2 00:30:00 20438
3 00:45:00 19012
4 01:00:00 18082
... ... ...
91 22:45:00 32365
92 23:00:00 31815
93 23:15:00 29582
94 23:30:00 26903
95 23:45:00 24599
Not sure why this index column appeared, now I failed to rename it or convert.
What do I would like to see?
My ideal output - to group time by hour (24h format is ok), it looks like data counts every 15 min, so basically put each next 4 columns together. 00:15:00 is ok to be as 0 hour, 23:00:00 as 23rd hour.
My ideal output:
Hour Rides
0 34000
1 60000
2 30000
3 40000
I would like to create afterwards a simple histogram to show the occurrence by the hour.
Appreciate any help!

IIUC,
#Create dummy input datafframe
test = pd.DataFrame({'time':pd.date_range('2020-06-01', '2020-06-01 23:59:00', freq='15T').strftime('%H:%M:%S'),
'rides':np.random.randint(15000,28000,96)})
Let's create a DateTimeIndex from string and resample, aggregate with sum and convert DateTimeIndex to hours:
test2 = (test.set_index(pd.to_datetime(test['time'], format='%H:%M:%S'))
.rename_axis('hour').resample('H').sum())
test2.index = test2.index.hour
test2.reset_index()
Output:
hour rides
0 0 74241
1 1 87329
2 2 76933
3 3 86208
4 4 88002
5 5 82618
6 6 82188
7 7 81203
8 8 78591
9 9 95592
10 10 99778
11 11 85294
12 12 93931
13 13 80490
14 14 84181
15 15 71786
16 16 90962
17 17 96568
18 18 85646
19 19 88324
20 20 83595
21 21 89284
22 22 72061
23 23 74057

Step by step I found answer myself
Using this code, I renamed columns
test.rename(columns = {'index': "Time", 'Start Time': 'Rides'})
Got
The remaining question - how to summarize by the hour.
After applying
test2['hour'] = pd.to_datetime(test2['Time'], format='%H:%M:%S').dt.hour
test2
I came closer
Finally, I grouped by hour value
test3 = test2.groupby('hour', as_index=False).agg({"Rides": "sum"})
print(test3)

Pandas dataframe: omit weekends and days near holidays

I have a Pandas dataframe with a DataTimeIndex and some other columns, similar to this:
import pandas as pd
import numpy as np
range = pd.date_range('2017-12-01', '2018-01-05', freq='6H')
df = pd.DataFrame(index = range)
# Average speed in miles per hour
df['value'] = np.random.randint(low=0, high=60, size=len(df.index))
df.info()
# DatetimeIndex: 141 entries, 2017-12-01 00:00:00 to 2018-01-05 00:00:00
# Freq: 6H
# Data columns (total 1 columns):
# value 141 non-null int64
# dtypes: int64(1)
# memory usage: 2.2 KB
df.head(10)
# value
# 2017-12-01 00:00:00 15
# 2017-12-01 06:00:00 54
# 2017-12-01 12:00:00 19
# 2017-12-01 18:00:00 13
# 2017-12-02 00:00:00 35
# 2017-12-02 06:00:00 31
# 2017-12-02 12:00:00 58
# 2017-12-02 18:00:00 6
# 2017-12-03 00:00:00 8
# 2017-12-03 06:00:00 30
How can I select or filter the entries that are:
Weekdays only (that is, not weekend days Saturday or Sunday)
Not within N days of the dates in a list (e.g. U.S. holidays like '12-25' or '01-01')?
I was hoping for something like:
df = exclude_Sat_and_Sun(df)
omit_days = ['12-25', '01-01']
N = 3 # days near the holidays
df = exclude_days_near_omit_days(N, omit_days)
I was thinking of creating a new column to break out the month and day and then comparing them to the criteria for 1 and 2 above. However, I was hoping for something more Pythonic using the DateTimeIndex.
Thanks for any help.

The first part can be easily accomplished using the Pandas DatetimeIndex.dayofweek property, which starts counting weekdays with Monday as 0 and ending with Sunday as 6.
df[df.index.dayofweek < 5] will give you only the weekdays.
For the second part you can use the datetime module. Below I will give an example for only one date, namely 2017-12-25. You can easily generalize it to a list of dates, for example by defining a helper function.
from datetime import datetime, timedelta
N = 3
df[abs(df.index.date - datetime.strptime("2017-12-25", '%Y-%m-%d').date()) > timedelta(N)]
This will give all dates that are more than N=3 days away from 2017-12-25. That is, it will exclude an interval of 7 days from 2017-12-22 to 2017-12-28.
Lastly, you can combine the two criteria using the & operator, as you probably know.
df[
(df.index.dayofweek < 5)
&
(abs(df.index.date - datetime.strptime("2017-12-25", '%Y-%m-%d').date()) > timedelta(N))
]

I followed the answer by #Bahman Engheta and created a function to omit dates from a dataframe.
import pandas as pd
from datetime import datetime, timedelta
def omit_dates(df, list_years, list_dates, omit_days_near=3, omit_weekends=False):
'''
Given a Pandas dataframe with a DatetimeIndex, remove rows that have a date
near a given list of dates and/or a date on a weekend.
Parameters:
----------
df : Pandas dataframe
list_years : list of str
Contains a list of years in string form
list_dates : list of str
Contains a list of dates in string form encoded as MM-DD
omit_days_near : int
Threshold of days away from list_dates to remove. For example, if
omit_days_near=3, then omit all days that are 3 days away from
any date in list_dates.
omit_weekends : bool
If true, omit dates that are on weekends.
Returns:
-------
Pandas dataframe
New resulting dataframe with dates omitted.
'''
if not isinstance(df, pd.core.frame.DataFrame):
raise ValueError("df is expected to be a Pandas dataframe, not %s" % type(df).__name__)
if not isinstance(df.index, pd.tseries.index.DatetimeIndex):
raise ValueError("Dataframe is expected to have an index of DateTimeIndex, not %s" %
type(df.index).__name__)
if not isinstance(list_years, list):
list_years = [list_years]
if not isinstance(list_dates, list):
list_dates = [list_dates]
result = df.copy()
if omit_weekends:
result = result.loc[result.index.dayofweek < 5]
omit_dates = [ '%s-%s' % (year, date) for year in list_years for date in list_dates ]
for date in omit_dates:
result = result.loc[abs(result.index.date - datetime.strptime(date, '%Y-%m-%d').date()) > timedelta(omit_days_near)]
return result
Here is example usage. Suppose you have a dataframe that has a DateTimeIndex and other columns, like this:
import pandas as pd
import numpy as np
range = pd.date_range('2017-12-01', '2018-01-05', freq='1D')
df = pd.DataFrame(index = range)
df['value'] = np.random.randint(low=0, high=60, size=len(df.index))
The resulting dataframe looks like this:
value
2017-12-01 42
2017-12-02 35
2017-12-03 49
2017-12-04 25
2017-12-05 19
2017-12-06 28
2017-12-07 21
2017-12-08 57
2017-12-09 3
2017-12-10 57
2017-12-11 46
2017-12-12 20
2017-12-13 7
2017-12-14 5
2017-12-15 30
2017-12-16 57
2017-12-17 4
2017-12-18 46
2017-12-19 32
2017-12-20 48
2017-12-21 55
2017-12-22 52
2017-12-23 45
2017-12-24 34
2017-12-25 42
2017-12-26 33
2017-12-27 17
2017-12-28 2
2017-12-29 2
2017-12-30 51
2017-12-31 19
2018-01-01 6
2018-01-02 43
2018-01-03 11
2018-01-04 45
2018-01-05 45
Now, let's specify dates to remove. I want to remove the dates '12-10', '12-25', '12-31', and '01-01' (following MM-DD notation) and all dates within 2 days of those dates. Further, I want to remove those dates from both the years '2016' and '2017'. I also want to remove weekend dates.
I'll call my function like this:
years = ['2016', '2017']
holiday_dates = ['12-10', '12-25', '12-31', '01-01']
omit_dates(df, years, holiday_dates, omit_days_near=2, omit_weekends=True)
The result is:
value
2017-12-01 42
2017-12-04 25
2017-12-05 19
2017-12-06 28
2017-12-07 21
2017-12-13 7
2017-12-14 5
2017-12-15 30
2017-12-18 46
2017-12-19 32
2017-12-20 48
2017-12-21 55
2017-12-22 52
2017-12-28 2
2018-01-03 11
2018-01-04 45
2018-01-05 45
Is that answer correct? Here are the calendars for December 2017 and January 2018:
December 2017
Su Mo Tu We Th Fr Sa
1 2
3 4 5 6 7 8 9
10 11 12 13 14 15 16
17 18 19 20 21 22 23
24 25 26 27 28 29 30
31
January 2018
Su Mo Tu We Th Fr Sa
1 2 3 4 5 6
7 8 9 10 11 12 13
14 15 16 17 18 19 20
21 22 23 24 25 26 27
28 29 30 31
Looks like it works.