This question already has answers here:
Wide to long data transform in pandas
(3 answers)
Closed 1 year ago.
I need to reshape a df and use the "year" information as a new column after reshaping. My data looks like this for df and will potentially contain more year data and players:
index player A 2012 player B 2012 player A 2013 player B 2013
0 15 10 20 35
1 40 25 60 70
My final df needs to look like this for dfnew:
index year player A player B
0 2012 15 10
0 2013 20 35
1 2012 40 25
1 2013 60 70
I"ve tried multiple variations of this code below and don't have a lot of experience in this but I don't know how to account for the changing "year" - i.e., 2012, 2013 and then to make that changing year into a new column.
df.pivot(index="index", columns=['player A','player B'])
Thank you very much,
Use wide_to_long:
df = pd.wide_to_long(df.reset_index(),
stubnames=['player A','player B'],
i='index',
j='Year',
sep=' ').reset_index(level=1).sort_index()
print (df)
Year player A player B
index
0 2012 15 10
0 2013 20 35
1 2012 40 25
1 2013 60 70
Or Series.str.rsplit by last space with DataFrame.stack:
df.columns = df.columns.str.rsplit(n=1, expand=True)
df = df.stack().rename_axis((None, 'Year')).reset_index(level=1)
print (df)
Year player A player B
0 2012 15 10
0 2013 20 35
1 2012 40 25
1 2013 60 70
Related
I have a rather simple request and have not found a suitable solution online. I have a DF that looks like this below and I need to find the cumulative deviation as shown in a new column to the DF. My DF looks like this:
year month Curr Yr LT Avg
0 2022 1 667590.5985 594474.2003
1 2022 2 701655.5967 585753.1173
2 2022 3 667260.5368 575550.6112
3 2022 4 795338.8914 562312.5309
4 2022 5 516510.1103 501330.4306
5 2022 6 465717.9192 418087.1358
6 2022 7 366100.4456 344854.2453
7 2022 8 355089.157 351539.9371
8 2022 9 468479.4396 496831.2979
9 2022 10 569234.4156 570767.1723
10 2022 11 719505.8569 594368.6991
11 2022 12 670304.78 576495.7539
And, I need the cumulative deviation new column in this DF to look like this:
Cum Dev
0.122993392
0.160154637
0.159888559
0.221628609
0.187604073
0.178089327
0.16687643
0.152866293
0.129326033
0.114260993
0.124487107
0.128058305
In Excel, the calculation would look like this with data in Excel columns Z3:Z14, AA3:AA14 for the first row: =SUM(Z$3:Z3)/SUM(AA$3:AA3)-1 and for the next row: =SUM(Z$3:Z4)/SUM(AA$3:AA4)-1 and for the next as follows with the last row looking like this in the Excel example: =SUM(Z$3:Z14)/SUM(AA$3:AA14)-1
Thank you kindly for your help,
You can divide the cumulative sums of those 2 columns element-wise, and then subtract 1 at the end:
>>> (df["Curr Yr"].cumsum() / df["LT Avg"].cumsum()) - 1
0 0.122993
1 0.160155
2 0.159889
3 0.221629
4 0.187604
5 0.178089
6 0.166876
7 0.152866
8 0.129326
9 0.114261
10 0.124487
11 0.128058
dtype: float64
I am trying to print the rows whereby a data condition is met in a pandas DF based on the unique values in the DF. For example, I have data that looks like this:
DF:
site temp month day
A 15 7 18
A 11 6 12
A 22 9 3
B 9 4 23
B 3 2 11
B -1 5 18
I need the result to print the rows where the max in the 'temp' column occurs such as this for the final result:
A 15
B 9
I have tried this but it is not working correctly:
for i in DF['site'].unique():
print(DF.temp.max())
I get the same answer of:
22
22
but the answer should be:
site temp month day
A 22 9 3
B 9 4 23
thank you!
A possible solution:
df.groupby('site', as_index=False).max()
Output:
site temp
0 A 22
1 B 9
In case you want to use a for loop:
for i in df['site'].unique():
print(df.loc[df['site'].eq(i), 'temp'].max())
Output:
22
9
df.groupby('site').max()
output:
temp month day
site
A 22 9 18
B 9 5 23
Let us do sort_values + drop_duplicates
df = df.sort_values('temp',ascending=False).drop_duplicates('site')
Out[190]:
site temp month day
2 A 22 9 3
3 B 9 4 23
I have the following data frame:
Month
Day
Year
Open
High
Low
Close
Week Close
Week
0
1
1
2003
46.593
46.656
46.405
46.468
45.593
1
1
1
2
2003
46.538
46.66
46.47
46.673
45.593
1
2
1
3
2003
46.717
46.781
46.53
46.750
45.593
1
3
1
4
2003
46.815
46.843
46.68
46.750
45.593
1
4
1
5
2003
46.935
47.000
46.56
46.593
45.593
1
...
...
...
...
...
...
...
...
...
7257
10
26
2022
381.619
387.5799
381.350
382.019
389.019
43
7258
10
27
2022
383.07
385.00
379.329
379.98
389.019
43
7259
10
28
2022
379.869
389.519
379.67
389.019
389.019
43
7260
10
31
2022
386.44
388.399
385.26
386.209
385.24
44
7261
11
1
2022
390.14
390.39
383.29
384.519
385.24
44
I want to create a new column titled 'Prior_Week_Close' which will reference the prior week's 'Week Close' value (and the last week of the prior year for the first week of every year). For example, row 7260's value for Prior_Week_Close should equal 389.019
I'm trying:
SPY['prior_week_close'] = np.where(SPY['Week'].shift(1) == (SPY['Week'] - 1), SPY['Week_Close'].shift(1), np.nan)
TypeError: boolean value of NA is ambiguous
I thought about just using shift and creating a new column but some weeks only have 4 days and that would lead to inaccurate values.
Any help is greatly appreciated!
I was able to solve this by creating a new column called 'Overall_Week' (the week number in the entire data set, not just the calendar year) and using the following code:
def fn(s):
result = SPY[SPY.Overall_Week == (s.iloc[0] - 1)]['Week_Close']
if result.shape[0] > 0:
return np.broadcast_to(result.iloc[0], s.shape)
else:
return np.broadcast_to(np.NaN, s.shape)
SPY['Prior_Week_Close'] = SPY.groupby('Overall_Week')['Overall_Week'].transform(fn)```
I want to drop both rows in a pandas data frame where the value in one column(account) is not duplicate and the value in some other column (recharge_number) is duplicate given A. An illustrative example:
data = {'account': [43,43,43,43,45,45],
'recharge_number': [17777, 17777, 17999, 17888, 17222, 17999] ,
'year': [2021,2021,2021,2021,2020,2020],
'month': [2,3,5,6,2,9]}
account recharge_number year month
43 17777 2021 2
43 17777 2021 3
43 17999 2021 5
43 17888 2021 6
45 17222 2020 2
45 17999 2020 9
input data
output:
account recharge_number year month
43 17777 2021 2
43 17777 2021 3
43 17888 2021 6
45 17222 2020 2
output data
Another method is to drop rows instead of keep them:
>>> df.drop(df[~df.duplicated(['id', 'number'], keep=False)
& df.duplicated('number', keep=False)].index)
id number
0 5 10
1 5 10
3 6 20
5 7 40
The first condition protect all duplicate ('id', 'number') records. The second condition remove all records where 'number' are the same.
Basically, you want "the full row (or the two columns if larger dataframe) is duplicated" or "number is not duplicated"
You can use duplicated:
df[df['id', 'number'].duplicated(keep=False)|~df['number'].duplicated(keep=False)]
Output:
id number
0 5 10
1 5 10
3 6 20
5 7 40
Solution with .crosstab:
mask = pd.crosstab(df["account"], df["recharge_number"]).ne(0).sum().gt(1)
print(df[~df["recharge_number"].isin(mask[mask].index)])
Prints:
account recharge_number year month
0 43 17777 2021 2
1 43 17777 2021 3
3 43 17888 2021 6
4 45 17222 2020 2
This question already has answers here:
Pandas index column title or name
(9 answers)
Closed 2 years ago.
I need to set index to my rows, and when I do that, pandas automatically makes my column index hierarchical..and then I tried every flatten mathod I can search, but once I reset_index, my index for row are replaced with iloc (integers). If I use df.columns = [ my col index name], it doesn't flatten my columns' index at all..
I use pandas official docs as example
df = pd.DataFrame({'month': [1, 4, 7, 10],
'year': [2012, 2014, 2013, 2014],
'sale': [55, 40, 84, 31]})
df.set_index('month')
and I get
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Then I flatten the index by
df.reset_index()
Then it becomes
index month year sale
0 0 1 2012 55
1 1 4 2014 40
2 2 7 2013 84
3 3 10 2014 31
(The month for row index disappeard...)
This really kills me so Im appreciate it if someone can help to make the dataframe to sth like
month year sale
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Thanks!
You only need to
df.reset_index(drop=True)
which returns
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31