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.
Related
I am currently trying to add totals to the bottom of my columns in preparation for my dataframes to be exported to excel/CSV files. I wanted to know what the best way to do this is.
I had been using
Wilhelm['Amount'].sum(), but this is not very efficient as I have to re-add them after I export every time.
Thank you!
Try this:
df.loc['Total'] = df.sum()
Let's say you have a dataframe of the form:
Jan Feb Mar Apr May Jun
Budget
Milk 10 20 31 52 7 11
Eggs 1 5 1 16 4 58
Bread 22 36 17 8 21 16
Butter 4 5 8 11 36 2
And you would like to add a Total row at the bottom which contains the sum of the columns. This is how I would do this task.
# Append a new row containing sum of each column
df.append(pd.Series(df.sum(numeric_only= True),name='Total'))
This will produce the dataframe of the following format.
Jan Feb Mar Apr May Jun
Budget
Milk 10 20 31 52 7 11
Eggs 1 5 1 16 4 58
Bread 22 36 17 8 21 16
Butter 4 5 8 11 36 2
Total 37 66 57 87 68 87
As was pointed out by #kevin-choon-liang-yew, append is scheduled for depreciation, in which case you would need to use the concat function to append a new row as shown below:
pd.concat([df, pd.DataFrame(pd.Series(df.sum(numeric_only= True)).to_dict(),index=['Total'])])
returning the final dataframe.
I have a table from different companies' sales.
company_name sales year
A 200 2019
A 100 2018
A 30 2017
B 15 2019
B 30 2018
B 45 2017
Now, I want to add a previous year's sales in the same row just like
company_name sales year previous_sales
A 200 2019 100
A 100 2018 30
A 30 2017 Nan
B 15 2019 30
B 30 2018 45
B 45 2017 Nan
I tried to use the code like this, but I failed to get the right result
df["previous_sales"] = df.groupby(['company_name', 'year'])['sales'].shift()
I have a data sheet with about 1700 columns and 100 rows of data w/ a unique identifier. It is survey data and every employee of an organization answer the same 9 questions but its compiled into one row of data for every organization. Is there a way in python/pandas to vertically integrate this data as opposed to the elongated format on the x-axis it already is at? I am cutting and pasting currently.
You can reshape the underlying numpy array and reindex with proper companies:
# sample data, assuming index is the company
df = pd.DataFrame(np.arange(36).reshape(2,-1))
# new index
idx = df.index.repeat(df.shape[1]//9)
# new data:
new_df = pd.DataFrame(df.values.reshape(-1,9), index=idx)
Output:
0 1 2 3 4 5 6 7 8
0 0 1 2 3 4 5 6 7 8
0 9 10 11 12 13 14 15 16 17
1 18 19 20 21 22 23 24 25 26
1 27 28 29 30 31 32 33 34 35
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'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