i have a dataframe that contains cell phone minutes usage logged by date of call and duration.
It looks like this (30 row sample):
id user_id call_date duration
0 1000_93 1000 2018-12-27 8.52
1 1000_145 1000 2018-12-27 13.66
2 1000_247 1000 2018-12-27 14.48
3 1000_309 1000 2018-12-28 5.76
4 1000_380 1000 2018-12-30 4.22
5 1000_388 1000 2018-12-31 2.20
6 1000_510 1000 2018-12-27 5.75
7 1000_521 1000 2018-12-28 14.18
8 1000_530 1000 2018-12-28 5.77
9 1000_544 1000 2018-12-26 4.40
10 1000_693 1000 2018-12-31 4.31
11 1000_705 1000 2018-12-31 12.78
12 1000_735 1000 2018-12-29 1.70
13 1000_778 1000 2018-12-28 3.29
14 1000_826 1000 2018-12-26 9.96
15 1000_842 1000 2018-12-27 5.85
16 1001_0 1001 2018-09-06 10.06
17 1001_1 1001 2018-10-12 1.00
18 1001_2 1001 2018-10-17 15.83
19 1001_4 1001 2018-12-05 0.00
20 1001_5 1001 2018-12-13 6.27
21 1001_6 1001 2018-12-04 7.19
22 1001_8 1001 2018-11-17 2.45
23 1001_9 1001 2018-11-19 2.40
24 1001_11 1001 2018-11-09 1.00
25 1001_13 1001 2018-12-24 0.00
26 1001_19 1001 2018-11-15 30.00
27 1001_20 1001 2018-09-21 5.75
28 1001_23 1001 2018-10-27 0.98
29 1001_26 1001 2018-10-28 5.90
30 1001_29 1001 2018-09-30 14.78
I want to group by user_id AND call_date with the ultimate goal of calculating the number of minutes used per month over the course of the year, per user.
I thought i could accomplish this by using:
calls.groupby(['user_id','call_date'])['duration'].sum()
but the results aren't what i expected:
user_id call_date
1000 2018-12-26 14.36
2018-12-27 48.26
2018-12-28 29.00
2018-12-29 1.70
2018-12-30 4.22
2018-12-31 19.29
1001 2018-08-14 13.86
2018-08-16 23.46
2018-08-17 8.11
2018-08-18 1.74
2018-08-19 10.73
2018-08-20 7.32
2018-08-21 0.00
2018-08-23 8.50
2018-08-24 8.63
2018-08-25 35.39
2018-08-27 10.57
2018-08-28 19.91
2018-08-29 0.54
2018-08-31 22.38
2018-09-01 7.53
2018-09-02 10.27
2018-09-03 30.66
2018-09-04 0.00
2018-09-05 9.09
2018-09-06 10.06
i'd hoped that it would be grouped like user_id 1000, all calls for jan with duration summed, all calls for feb with duration summed, etc.
i am really new to python and programming in general and am not sure what my next step should be to get these grouped by user_id and month of the year?
Thanks in advance for any insight you can offer.
Regards,
Jared
Something is not quite right in your setup. First of all, both of your tables are the same, so I am not sure if this is a cut-and-paste error or something else. Here is what I do with your data. Load it up like so, note we explicitly convert call_date to Datetime`
from io import StringIO
import pandas as pd
df = pd.read_csv(StringIO(
"""
id user_id call_date duration
0 1000_93 1000 2018-12-27 8.52
1 1000_145 1000 2018-12-27 13.66
2 1000_247 1000 2018-12-27 14.48
3 1000_309 1000 2018-12-28 5.76
4 1000_380 1000 2018-12-30 4.22
5 1000_388 1000 2018-12-31 2.20
6 1000_510 1000 2018-12-27 5.75
7 1000_521 1000 2018-12-28 14.18
8 1000_530 1000 2018-12-28 5.77
9 1000_544 1000 2018-12-26 4.40
10 1000_693 1000 2018-12-31 4.31
11 1000_705 1000 2018-12-31 12.78
12 1000_735 1000 2018-12-29 1.70
13 1000_778 1000 2018-12-28 3.29
14 1000_826 1000 2018-12-26 9.96
15 1000_842 1000 2018-12-27 5.85
16 1001_0 1001 2018-09-06 10.06
17 1001_1 1001 2018-10-12 1.00
18 1001_2 1001 2018-10-17 15.83
19 1001_4 1001 2018-12-05 0.00
20 1001_5 1001 2018-12-13 6.27
21 1001_6 1001 2018-12-04 7.19
22 1001_8 1001 2018-11-17 2.45
23 1001_9 1001 2018-11-19 2.40
24 1001_11 1001 2018-11-09 1.00
25 1001_13 1001 2018-12-24 0.00
26 1001_19 1001 2018-11-15 30.00
27 1001_20 1001 2018-09-21 5.75
28 1001_23 1001 2018-10-27 0.98
29 1001_26 1001 2018-10-28 5.90
30 1001_29 1001 2018-09-30 14.78
"""), delim_whitespace = True, index_col=0)
df['call_date'] = pd.to_datetime(df['call_date'])
Then using
df.groupby(['user_id','call_date'])['duration'].sum()
does the expected grouping by user and by each date:
user_id call_date
1000 2018-12-26 14.36
2018-12-27 48.26
2018-12-28 29.00
2018-12-29 1.70
2018-12-30 4.22
2018-12-31 19.29
1001 2018-09-06 10.06
2018-09-21 5.75
2018-09-30 14.78
2018-10-12 1.00
2018-10-17 15.83
2018-10-27 0.98
2018-10-28 5.90
2018-11-09 1.00
2018-11-15 30.00
2018-11-17 2.45
2018-11-19 2.40
2018-12-04 7.19
2018-12-05 0.00
2018-12-13 6.27
2018-12-24 0.00
If you want to group by month as you seem to suggest you can use the Grouper functionality:
df.groupby(['user_id',pd.Grouper(key='call_date', freq='1M')])['duration'].sum()
which produces
user_id call_date
1000 2018-12-31 116.83
1001 2018-09-30 30.59
2018-10-31 23.71
2018-11-30 35.85
2018-12-31 13.46
Let me know if you are getting different results from following these steps
Related
I have 2 types of text files which I need to read into a pandas DataFrame. I have a problem with the datetimes and separator.
File A:
2009,7,1,3,101,13.03,89.33,0.6,287.69,0
2009,7,1,6,102,19.3,55,1,288.67,0
2009,7,1,9,103,22.33,39.67,1,289.6,0
2009,7,1,12,104,21.97,41,1,295.68,0
File B:
2019 9 1 3.00 101 14.02 92.08 2.62 174.77 0.109
2019 9 1 6.00 102 13.79 92.86 2.79 179.29 0.046
2019 9 1 9.00 103 13.81 92.60 2.73 178.94 0.070
2019 9 1 12.00 104 13.31 95.20 2.91 179.38 0.015
fileA.txt has no extra spaces, fileB.txt has 1 extra space on the beginning of each line. I can read each of these as follows and the results are correct:
>>> import pandas as pd
>>> from datetime import datetime as dtdt
>>> par3 = lambda x: dtdt.strptime(x, '%Y %m %d %H')
>>> par4 = lambda x: dtdt.strptime(x, '%Y %m %d %H.%M')
>>> df3=pd.read_csv('fileA.txt',header=None,parse_dates={'Date': [0,1,2,3]}, date_parser=par3, index_col='Date')
>>> df3
4 5 6 7 8 9
Date
2009-07-01 03:00:00 101 13.03 89.33 0.6 287.69 0
2009-07-01 06:00:00 102 19.30 55.00 1.0 288.67 0
2009-07-01 09:00:00 103 22.33 39.67 1.0 289.60 0
2009-07-01 12:00:00 104 21.97 41.00 1.0 295.68 0
>>> dg3=pd.read_csv('fileB.txt',sep='\s+',engine='python',header=None,parse_dates={'Date': [0,1,2,3]}, date_parser=par4, index_col='Date')
>>> dg3
4 5 6 7 8 9
Date
2019-09-01 03:00:00 101 14.02 92.08 2.62 174.77 0.109
2019-09-01 06:00:00 102 13.79 92.86 2.79 179.29 0.046
2019-09-01 09:00:00 103 13.81 92.60 2.73 178.94 0.070
2019-09-01 12:00:00 104 13.31 95.20 2.91 179.38 0.015
Question: how do I read in both of these types with the same command? The only way I can think of is to first open the file and read in first line to deduce the hour-format (col.3) and the separator? That feels non-pythonic way.
Also, if the hour-reading float is e.g. 3.75, it would be ok to round that into nearest integer and just set the minute reading to 0.
I have the following dataframe:
Timestamp userid Prices_USD
0 2016-12-01 6.270941895 1.08
1 2016-12-01 6.609813209 1.12
2 2016-12-01 6.632094115 9.70
3 2016-12-01 6.655789772 1.08
4 2016-12-01 6.764640751 9.33
... ... ... ...
1183 2017-03-27 6.529604089 1.08
1184 2017-03-27 6.682639674 6.72
1185 2017-03-27 6.773815105 10.0
I want to calculate, for each unique userid, their monthly spending.
I've tried the following:
sales_per_user.set_index('Timestamp',inplace=True)
sales_per_user.index = pd.to_datetime(sales_per_user.index)
m = sales_per_user.index.month
monthly_avg = sales_per_user.groupby(['userid', m]).Prices_USD.mean().to_frame()
But the resulting dataframe is this:
userid Timestamp Prices_USD
3.43964843 12 10.91
3.885813375 1 10.91
2 10.91
12 21.82
However, the timestamp column doesn't have the desired outcome. Ideally I would like
userid Timestamp Prices_USD
3.43964843 2016-12 10.91
3.885813375 2017-01 10.91
2017-02 10.91
2017-12 21.82
How do I fix that?
Try:
df['Timestamp'] = pd.to_datetime(df['Timestamp'])
res = df.groupby([df['userid'], df['Timestamp'].dt.to_period('M')])['Prices_USD'].sum()
print(res)
Output
userid Timestamp
6.270942 2016-12 1.08
6.529604 2017-03 1.08
6.609813 2016-12 1.12
6.632094 2016-12 9.70
6.655790 2016-12 1.08
6.682640 2017-03 6.72
6.764641 2016-12 9.33
6.773815 2017-03 10.00
Name: Prices_USD, dtype: float64
I need to find cases where "price of y" was less than 3.5 until time 30:00
and after that when "price of x" jump above 3.5.
I made column of "Demical Time" to make it easier for me (less than 30:00 is less than 1800 sec in Demical)
I tried to find all the cases which price of y was under 3.5 (and above 0) but I failed to write code which gives the cases where price of y was under 3.5 AND price of x was greater than 3.5 after 30:00.
df1 = df[(df['price_of_Y']<3.5)&(df['price_of_Y']>0)& (df['Demical time']<1800)]
#the cases for price of y under 3.5 before time is 30:00 (Demical time =1800)
df2 = df[(df['price_of_X']>3.5) & (df['Demical time'] >1800 )]`
#the cases for price of x above 3.5 after time is 30:00 (Demical time =1800)
# the question is how do i combine them to one line?
price_of_X time price_of_Y Demical time
0 3.30 0 4.28 0
1 3.30 0:00 4.28 0
2 3.30 0:00 4.28 0
3 3.30 0:00 4.28 0
4 3.30 0:00 4.28 0
5 3.30 0:00 4.28 0
6 3.30 0:00 4.28 0
7 3.30 0:00 4.28 0
8 3.30 0:00 4.28 0
9 3.30 0:00 4.28 0
10 3.30 0:00 4.28 0
11 3.25 0:26 4.28 26
12 3.40 1:43 4.28 103
13 3.25 3:00 4.28 180
14 3.25 4:16 4.28 256
15 3.40 5:34 4.28 334
16 3.40 6:52 4.28 412
17 3.40 8:09 4.28 489
18 3.40 9:31 4.28 571
19 5.00 10:58 8.57 658
20 5.00 12:13 8.57 733
21 5.00 13:31 7.38 811
22 5.00 14:47 7.82 887
23 5.00 16:01 7.82 961
24 5.00 17:18 7.38 1038
25 5.00 18:33 7.38 1113
26 5.00 19:50 7.38 1190
27 5.00 21:09 7.38 1269
28 5.00 22:22 7.38 1342
29 5.00 23:37 8.13 1417
... ... ... ... ...
18138 7.50 59:03:00 28.61 3543
18139 7.50 60:19:00 28.61 3619
18140 7.50 61:35:00 34.46 3695
18141 8.00 62:48:00 30.16 3768
18142 7.50 64:03:00 34.46 3843
18143 8.00 65:20:00 30.16 3920
18144 7.50 66:34:00 28.61 3994
18145 7.50 67:53:00 30.16 4073
18146 8.00 69:08:00 26.19 4148
18147 7.00 70:23:00 23.10 4223
18148 7.00 71:38:00 23.10 4298
18149 8.00 72:50:00 30.16 4370
18150 7.50 74:09:00 26.19 4449
18151 7.50 75:23:00 25.58 4523
18152 7.00 76:40:00 19.07 4600
18153 7.00 77:53:00 19.07 4673
18154 9.00 79:11:00 31.44 4751
18155 9.00 80:27:00 27.11 4827
18156 10.00 81:41:00 34.52 4901
18157 10.00 82:56:00 34.52 4976
18158 11.00 84:16:00 43.05 5056
18159 10.00 85:35:00 29.42 5135
18160 10.00 86:49:00 29.42 5209
18161 11.00 88:04:00 35.70 5284
18162 13.00 89:19:00 70.38 5359
18163 15.00 90:35:00 70.42 5435
18164 19.00 91:48:00 137.70 5508
18165 23.00 93:01:00 511.06 5581
18166 NaN NaN NaN 0
18167 NaN NaN NaN 0
[18168 rows x 4 columns]
dataframe:
This should solve it.
I have used a bit different data and condition values, but you should get the idea of what i am doing.
import pandas as pd
df = pd.DataFrame({'price_of_X': [3.30,3.25,3.40,3.25,3.25,3.40],
'price_of_Y': [2.28,1.28,4.28,4.28,1.18,3.28],
'Decimal_time': [0,26,103,180,256,334]
})
print(df)
df1 = df.loc[(df['price_of_Y']<3.5)&(df['price_of_X']>3.3)&(df['Decimal_time']>103),:]
print(df1)
output:
df
price_of_X price_of_Y Decimal_time
0 3.30 2.28 0
1 3.25 1.28 26
2 3.40 4.28 103
3 3.25 4.28 180
4 3.25 1.18 256
5 3.40 3.28 334
df1
price_of_X price_of_Y Decimal_time
5 3.4 3.28 334
Similar to what #IMCoins suggested as a comment, use two boolean masks to achieve the selection that you require.
mask1 = (df['price_of_Y'] < 3.5) & (df['price_of_Y'] > 0) & (df['Demical time'] < 1800)
mask2 = (df['price_of_X'] > 3.5) & (df['Demical time'] > 1800)
df[mask1 | mask2]
I have two data frames. One has rows for every five minutes in a day:
df
TIMESTAMP TEMP
1 2011-06-01 00:05:00 24.5
200 2011-06-01 16:40:00 32.0
1000 2011-06-04 11:20:00 30.2
5000 2011-06-18 08:40:00 28.4
10000 2011-07-05 17:20:00 39.4
15000 2011-07-23 02:00:00 29.3
20000 2011-08-09 10:40:00 29.5
30656 2011-09-15 10:40:00 13.8
I have another dataframe that ranks the days
ranked
TEMP DATE RANK
62 43.3 2011-08-02 1.0
63 43.1 2011-08-03 2.0
65 43.1 2011-08-05 3.0
38 43.0 2011-07-09 4.0
66 42.8 2011-08-06 5.0
64 42.5 2011-08-04 6.0
84 42.2 2011-08-24 7.0
56 42.1 2011-07-27 8.0
61 42.1 2011-08-01 9.0
68 42.0 2011-08-08 10.0
Both the columns TIMESTAMP and DATE are datetime datatypes (dtype returns dtype('M8[ns]').
What I want to be able to do is add a column to the dataframe df and then put the rank of the row based on the TIMESTAMP and corresponding day's rank from ranked (so in a day all the 5 minute timesteps will have the same rank).
So, the final result would look something like this:
df
TIMESTAMP TEMP RANK
1 2011-06-01 00:05:00 24.5 98.0
200 2011-06-01 16:40:00 32.0 98.0
1000 2011-06-04 11:20:00 30.2 96.0
5000 2011-06-18 08:40:00 28.4 50.0
10000 2011-07-05 17:20:00 39.4 9.0
15000 2011-07-23 02:00:00 29.3 45.0
20000 2011-08-09 10:40:00 29.5 40.0
30656 2011-09-15 10:40:00 13.8 100.0
What I have done so far:
# Separate the date and times.
df['DATE'] = df['YYYYMMDDHHmm'].dt.normalize()
df['TIME'] = df['YYYYMMDDHHmm'].dt.time
df = df[['DATE', 'TIME', 'TAIR']]
df['RANK'] = 0
for index, row in df.iterrows():
df.loc[index, 'RANK'] = ranked[ranked['DATE']==row['DATE']]['RANK'].values
But I think I am going in a very wrong direction because this takes ages to complete.
How do I improve this code?
IIUC, you can play with indexes to match the values
df = df.set_index(df.TIMESTAMP.dt.date)\
.assign(RANK=ranked.set_index('DATE').RANK)\
.set_index(df.index)
I am trying to roll up daily data into fiscal quarter data. For example, I have a table with fiscal quarter end dates:
Company Period Quarter_End
M 2016Q1 05/02/2015
M 2016Q2 08/01/2015
M 2016Q3 10/31/2015
M 2016Q4 01/30/2016
WFM 2015Q2 04/12/2015
WFM 2015Q3 07/05/2015
WFM 2015Q4 09/27/2015
WFM 2016Q1 01/17/2016
and a table of daily data:
Company Date Price
M 06/20/2015 1.05
M 06/22/2015 4.05
M 07/10/2015 3.45
M 07/29/2015 1.86
M 08/24/2015 1.58
M 09/02/2015 8.64
M 09/22/2015 2.56
M 10/20/2015 5.42
M 11/02/2015 1.58
M 11/24/2015 4.58
M 12/03/2015 6.48
M 12/05/2015 4.56
M 01/03/2016 7.14
M 01/30/2016 6.34
WFM 06/20/2015 1.05
WFM 06/22/2015 4.05
WFM 07/10/2015 3.45
WFM 07/29/2015 1.86
WFM 08/24/2015 1.58
WFM 09/02/2015 8.64
WFM 09/22/2015 2.56
WFM 10/20/2015 5.42
WFM 11/02/2015 1.58
WFM 11/24/2015 4.58
WFM 12/03/2015 6.48
WFM 12/05/2015 4.56
WFM 01/03/2016 7.14
WFM 01/17/2016 6.34
And I would like to create the table below.
Company Period Quarter_end Sum(Price)
M 2016Q2 8/1/2015 10.41
M 2016Q3 10/31/2015 18.2
M 2016Q4 1/30/2016 30.68
WFM 2015Q3 7/5/2015 5.1
WFM 2015Q4 9/27/2015 18.09
WFM 2016Q1 1/17/2016 36.1
However, I don't know how to group by varying dates without looping through each record. Any help is greatly appreciated.
Thanks!
I think you can use merge_ordered:
#first convert columns to datetime
df1.Quarter_End = pd.to_datetime(df1.Quarter_End)
df2.Date = pd.to_datetime(df2.Date)
df = pd.merge_ordered(df1,
df2,
left_on=['Company','Quarter_End'],
right_on=['Company','Date'],
how='outer')
print (df)
Company Period Quarter_End Date Price
0 M 2016Q1 2015-05-02 NaT NaN
1 M NaN NaT 2015-06-20 1.05
2 M NaN NaT 2015-06-22 4.05
3 M NaN NaT 2015-07-10 3.45
4 M NaN NaT 2015-07-29 1.86
5 M 2016Q2 2015-08-01 NaT NaN
6 M NaN NaT 2015-08-24 1.58
7 M NaN NaT 2015-09-02 8.64
8 M NaN NaT 2015-09-22 2.56
9 M NaN NaT 2015-10-20 5.42
10 M 2016Q3 2015-10-31 NaT NaN
11 M NaN NaT 2015-11-02 1.58
12 M NaN NaT 2015-11-24 4.58
13 M NaN NaT 2015-12-03 6.48
14 M NaN NaT 2015-12-05 4.56
15 M NaN NaT 2016-01-03 7.14
16 M 2016Q4 2016-01-30 2016-01-30 6.34
17 WFM 2015Q2 2015-04-12 NaT NaN
18 WFM NaN NaT 2015-06-20 1.05
19 WFM NaN NaT 2015-06-22 4.05
20 WFM 2015Q3 2015-07-05 NaT NaN
21 WFM NaN NaT 2015-07-10 3.45
22 WFM NaN NaT 2015-07-29 1.86
23 WFM NaN NaT 2015-08-24 1.58
24 WFM NaN NaT 2015-09-02 8.64
25 WFM NaN NaT 2015-09-22 2.56
26 WFM 2015Q4 2015-09-27 NaT NaN
27 WFM NaN NaT 2015-10-20 5.42
28 WFM NaN NaT 2015-11-02 1.58
29 WFM NaN NaT 2015-11-24 4.58
30 WFM NaN NaT 2015-12-03 6.48
31 WFM NaN NaT 2015-12-05 4.56
32 WFM NaN NaT 2016-01-03 7.14
33 WFM 2016Q1 2016-01-17 2016-01-17 6.34
Then backfill NaN in columns Period and Quarter_End by bfill and aggregate sum. If need remove all NaN values, add Series.dropna and last reset_index:
df.Period = df.Period.bfill()
df.Quarter_End = df.Quarter_End.bfill()
print (df.groupby(['Company','Period','Quarter_End'])['Price'].sum().dropna().reset_index())
Company Period Quarter_End Price
0 M 2016Q2 2015-08-01 10.41
1 M 2016Q3 2015-10-31 18.20
2 M 2016Q4 2016-01-30 30.68
3 WFM 2015Q3 2015-07-05 5.10
4 WFM 2015Q4 2015-09-27 18.09
5 WFM 2016Q1 2016-01-17 36.10
set_index
pd.concat to align indices
groupby with agg
prd_df = period_df.set_index(['Company', 'Quarter_End'])
prc_df = price_df.set_index(['Company', 'Date'], drop=False)
df = pd.concat([prd_df, prc_df], axis=1)
df.groupby([df.index.get_level_values(0), df.Period.bfill()]) \
.agg(dict(Date='last', Price='sum')).dropna()