I'm trying to turn the following dataframe (with values for county and year)
county region 2012 2013 ... 2035
A 101 10 15 ... 7
B 101 13 8 ... 11
...
into a dataframe that looks like this:
county region year sum
A 101 2012 10
A 101 2013 15
... ... ... ...
A 101 2035 7
B 101 2012 13
B 101 2013 8
B 101 2035 11
My current dataframe has 400 rows (different counties) with values for the years 2012-2035.
My manual approach would be to slice the year columns off and put each of them below the last row of the preceding year. But of course there has to be a pythonic way.
I guess I'm missing a basic pandas concept here, probably I just couldn't find the right answer to this problem because I simply didn't know how to ask the right question. Please be gentle with the newcomer.
You can use melt from pandas:
In [26]: df
Out[26]:
county region 2012 2013
0 A 101 10 15
1 B 101 13 8
In [27]: pd.melt(df, id_vars=['county','region'], var_name='year', value_name='sum')
Out[27]:
county region year sum
0 A 101 2012 10
1 B 101 2012 13
2 A 101 2013 15
3 B 101 2013 8
Related
I have the following data frame:
Month
Day
Year
Open
High
Low
Close
Week
0
1
1
2003
46.593
46.656
46.405
46.468
1
1
1
2
2003
46.538
46.66
46.47
46.673
1
2
1
3
2003
46.717
46.781
46.53
46.750
1
3
1
4
2003
46.815
46.843
46.68
46.750
1
4
1
5
2003
46.935
47.000
46.56
46.593
1
...
...
...
...
...
...
...
...
...
7257
10
26
2022
381.619
387.5799
381.350
382.019
43
7258
10
27
2022
383.07
385.00
379.329
379.98
43
7259
10
28
2022
379.869
389.519
379.67
389.019
43
7260
10
31
2022
386.44
388.399
385.26
386.209
44
7261
11
1
2022
390.14
390.39
383.29
384.519
44
I want to create a new column titled 'week high' which will reference each week every year and pull in the high. So for Week 1, Year 2003, it will take the Highest High from rows 0 to 4 but for Week 43, Year 2022, it will take the Highest High from rows 7257 to 7259.
Is it possible to reference the columns Week and Year to calculate that value? Thanks!
Assuming pandas, create a weekly period and use it as grouper for transform('max'):
group = pd.to_datetime(df[['Year', 'Month', 'Day']]).dt.to_period('W')
# or, if you already have a "Week" column
# group = "Week"
df['week_high'] = df.groupby(group)['High'].transform('max')
Output:
Month Day Year Open High Low Close Week week_high
0 1 1 2003 46.593 46.6560 46.405 46.468 1.0 47.000
1 1 2 2003 46.538 46.6600 46.470 46.673 1.0 47.000
2 1 3 2003 46.717 46.7810 46.530 46.750 1.0 47.000
3 1 4 2003 46.815 46.8430 46.680 46.750 1.0 47.000
4 1 5 2003 46.935 47.0000 46.560 46.593 1.0 47.000
7257 10 26 2022 381.619 387.5799 381.350 382.019 43.0 389.519
7258 10 27 2022 383.070 385.0000 379.329 379.980 43.0 389.519
7259 10 28 2022 379.869 389.5190 379.670 389.019 43.0 389.519
7260 10 31 2022 386.440 388.3990 385.260 386.209 44.0 390.390
7261 11 1 2022 390.140 390.3900 383.290 384.519 44 390.390
I am assuming you are using pandas. Other libraries will work similar.
Create a new DataFrame aggregated per week using groupby and join it back to your original DataFrame
df_grouped = df["Week", "High"].groupby("Week").max().rename(columns={"High":"Highest High"}
df_result = df.join(df_grouped, "Week")
I'm trying to add after the Gross profit line in an income statement new line with some values from array.
I tried just to append it in the location but nothing changed.
income_statement.loc[["Gross Profit"]].append(gross)
The only way i succeed doing something similar is by making it another dataframe and concat it to end of the income_statement.
I'm trying to make it look like that:(The 'gross' line in yellow)
How can i do it?
I created a sample df that tried to look similar to yours (see below).
df
Unnamed: 0 2010 2011 2012 2013 ... 2016 2017 2018 2019 TTM
0 gross profit 10 11 12 13 ... 16 17 18 19 300
1 total revenue 1 2 3 4 ... 7 8 9 10 400
The aim now would be to add a row between them ('gross'), with the values you have listed in the picture.
One way to add the row could be with numpy.insert, which returns an array back so you have to convert back to a pd.DataFrame:
# Store the columns of your df
cols = df.columns
# Add the row (the number indicates the index position for the row to be added,1 is the 2nd row as Python indexes start from 0)
new = pd.DataFrame(np.insert
(df.values, 1, values = ['gross',22, 45, 65,87,108,130,151,152,156,135,133], axis=0),
columns=cols)
Which gets back:
new
Unnamed: 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 TTM
0 gross profit 10 11 12 13 14 15 16 17 18 19 300
1 gross 22 45 65 87 108 130 151 152 156 135 133
2 total revenue 1 2 3 4 5 6 7 8 9 10 400
Hopefully this will work for you. Let me know for issues.
I'm quite new to programming, and I'm using Python it for data manipulation and analysis.
I have a dataframe that looks like:
Brand Date Unit
A 1/1/19 10
B 3/1/19 11
A 11/1/19 15
B 11/1/19 5
A 1/1/20 10
A 9/2/19 18
B 12/2/19 11
B 19/2/19 8
B 1/1/20 5
And I would like to group by month, year and Brand. If it helps, I also have separate columns for Month and Year. The expected result should look like this:
Brand Date Unit
A Jan 2019 25
B Jan 2019 16
A Feb 2019 18
B Feb 2019 19
A Jan 2020 8
B Feb 2020 5
I tried adapting an answer from someone else's question:
per = df.Date.dt.to_period("M")
g = df.groupby(per,'Brand')
g.sum()
but I get prompted:
ValueError: No axis named Brand for object type <class 'pandas.core.frame.DataFrame'>
and I don't have any idea how to solve this.
I used to do this with dictionaries by selecting each month/year individually, group by sum and then create the dictionary, but it seems kind of brute force, really rough and it won't help if the df gets updated with new data.
Even more, maybe I'm having a bad approach to the situation. In the end I'd like to have a df looking like:
Brand Jan 19 Feb 19 Jan 20
A 25 18 8
B 16 19 5
Use pandas.to_datetime and pandas.DataFrame.pivot_table:
df["Date"] = pd.to_datetime(df["Date"], dayfirst=True).dt.strftime("%b %Y")
new_df = df.pivot_table(index="Brand", columns="Date", aggfunc=sum)
print(new_df)
Output:
Unit
Date Feb 2019 Jan 2019 Jan 2020
Brand
A 18 25 10
B 19 16 5
You were close, DataFrame.groupby wants a list of groupers, not bare arguments.
Here's how I did it:
import pandas
from io import StringIO
csv = StringIO("""\
Brand Date Unit
A 1/1/19 10
B 3/1/19 11
A 11/1/19 15
B 11/1/19 5
A 1/1/20 10
A 9/2/19 18
B 12/2/19 11
B 19/2/19 8
B 1/1/20 5
""")
(
pandas.read_csv(csv, parse_dates=['Date'], sep='\s+', dayfirst=True)
.groupby(['Brand', pandas.Grouper(key='Date', freq='1M')])
.sum()
.reset_index()
)
And that gives me:
Brand Date Unit
0 A 2019-01-31 25
1 A 2019-02-28 18
2 A 2020-01-31 10
3 B 2019-01-31 16
4 B 2019-02-28 19
5 B 2020-01-31 5
I have a DataFrame that looks like:
f_period f_year f_month subject month year value
20140102 2014 1 a 1 2018 10
20140109 2014 1 a 1 2018 12
20140116 2014 1 a 1 2018 8
20140202 2014 2 a 1 2018 20
20140209 2014 2 a 1 2018 15
20140102 2014 1 b 1 2018 10
20140109 2014 1 b 1 2018 12
20140116 2014 1 b 1 2018 8
20140202 2014 2 b 1 2018 20
20140209 2014 2 b 1 2018 15
The f_period is the date when a forecast for a SKU (column subject) was made. The month and year column is the period for which the forecast was made. For example, the first row says that on 01/02/2018, the model was forecasting to set 10 units of product a in month 1 of year2018.
I am trying to create a rolling average prediction by subject, by month for 2 f_months. The DataFrame should look like:
f_period f_year f_month subject month year value mnthly_avg rolling_2_avg
20140102 2014 1 a 1 2018 10 10 13
20140109 2014 1 a 1 2018 12 10 13
20140116 2014 1 a 1 2018 8 10 13
20140202 2014 2 a 1 2018 20 17.5 null
20140209 2014 2 a 1 2018 15 17.5 null
20140102 2014 1 b 1 2018 10 10 13
20140109 2014 1 b 1 2018 12 10 13
20140116 2014 1 b 1 2018 8 10 13
20140202 2014 2 b 1 2018 20 17.5 null
20140209 2014 2 b 1 2018 15 17.5 null
Things I tried:
I was able to get mnthly_avg by :
data_df['monthly_avg'] = data_df.groupby(['f_month', 'f_year', 'year', 'month', 'period', 'subject']).\
value.transform('mean')
I tried getting the rolling_2_avg :
rolling_monthly_df = data_df[['f_year', 'f_month', 'subject', 'month', 'year', 'value', 'f_period']].\
groupby(['f_year', 'f_month', 'subject', 'month', 'year']).value.mean().reset_index()
rolling_monthly_df['rolling_2_avg'] = rolling_monthly_df.groupby(['subject', 'month']).\
value.rolling(2).mean().reset_index(drop=True)
This gave me an unexpected output. I don't understand how it calculated the values for rolling_2_avg
How do I group by subject and month and then sort by f_month and then take the average of the next two-month average?
Unless I'm misunderstanding it seems simpler than what you've done. What about this?
grp = pd.DataFrame(df.groupby(['subject', 'month', 'f_month'])['value'].sum())
grp['rolling'] = grp.rolling(window=2).mean()
grp
Output:
value rolling
subject month f_month
a 1 1 30 NaN
2 35 32.5
b 1 1 30 32.5
2 35 32.5
I would be a bit careful with Josh's solution. If you want to group by the subject you can't use the rolling function like that as it will roll across subjects (i.e. it will eventually take the mean of a month from subject A and B, rather than giving a null which you might prefer).
An alternative can be to split the dataframe and run the rolling individually (I noticed that you want the nulls by the end of the dataframe, whereas you might wanna sort the dataframe before and after):
for unique_subject in df['subject'].unique():
df_subject = df[df['subject'] == unique_subject]
df_subject['rolling'] = df_subject['value'].rolling(window=2).mean()
print(df_subject) # just to print, you may wanna concatenate these
I have a DataFrame with a timestamp index and some 100,000 rows. Via
df['year'] = df.index.year
it is easy to create a new column which contains the year of each row. Now I want to find out which years are missing from my timeseries. So far, I understand that I can use groupby to obtain "something" which allows me to find the unique values. Thus,
grouped = df.groupby('year')
grouped.groups.keys()
will give me the years which are present in my dataset. I could now build a complete year vector with
pd.date_range(df.index.min(), df.index.max(), freq='AS')
and through reindex I should then be able to find the missing years as those years which have NaN values.
However, this sounds awfully complicated for such seemingly simple task, and the grouped.groups operation actually takes quite a while; presumably, because it doesn't only look for unique keys, but also builds the index lists of rows that belong to each key, which is a feature that I don't need here.
Is there any way to obtain the unique elements of a dataframe column more directly/efficiently?
One method would be to construct a series of the years of interest and then use isin to see the missing values:
In [89]:
year_s = pd.Series(np.arange(1993, 2015))
year_s
Out[89]:
0 1993
1 1994
2 1995
3 1996
4 1997
5 1998
6 1999
7 2000
8 2001
9 2002
10 2003
11 2004
12 2005
13 2006
14 2007
15 2008
16 2009
17 2010
18 2011
19 2012
20 2013
21 2014
dtype: int32
In [88]:
df = pd.DataFrame({'year':[1999, 2000, 2013]})
df
Out[88]:
year
0 1999
1 2000
2 2013
In [91]:
year_s[~year_s.isin(df['year'])]
Out[91]:
0 1993
1 1994
2 1995
3 1996
4 1997
5 1998
8 2001
9 2002
10 2003
11 2004
12 2005
13 2006
14 2007
15 2008
16 2009
17 2010
18 2011
19 2012
21 2014
dtype: int32
So in your case you can generate the year series as above, then for your df you can get the years using:
df.index.year.unique()
which will be much quicker than performing a groupby.
Take care that the last value passed to arange is not included in the range
If all you want is a list of missing years, you can first convert your Data Series to a list and simply build a list of missing years using a list comprehension:
years = df['year'].unique()
missing_years = [y for y in range(min(years), max(years)+1) if y not in years]