Unstack a dataframe with duplicated index in Pandas - python-3.x

Given a toy dataset as follow which has duplicated price and quantity:
city item value
0 bj price 12
1 bj quantity 15
2 bj price 12
3 bj quantity 15
4 bj level a
5 sh price 45
6 sh quantity 13
7 sh price 56
8 sh quantity 7
9 sh level b
I want to reshape it into the following dataframe, which means add sell_ for the first pair and buy_ for the second pair:
city sell_price sell_quantity buy_price buy_quantity level
0 bj 12 15 13 16 a
1 sh 45 13 56 7 b
I have tried with df.set_index(['city', 'item']).unstack().reset_index(), but it raises an error: ValueError: Index contains duplicate entries, cannot reshape.
How could I get the desired output as above? Thanks.


You can add for second duplicated values buy_ and for first duplicates sell_ and change values in item before your solution:
m1 = df.duplicated(['city', 'item'])
m2 = df.duplicated(['city', 'item'], keep=False)
df['item'] = np.where(m1, 'buy_', np.where(m2, 'sell_', '')) + df['item']
df = (df.set_index(['city', 'item'])['value']
.unstack()
.reset_index()
.rename_axis(None, axis=1))
#for change order of columns names
df = df[['city','sell_price','sell_quantity','buy_price','buy_quantity','level']]
print (df)
city sell_price sell_quantity buy_price buy_quantity level
0 bj 12 15 12 15 a
1 sh 45 13 56 7 b

Related

How to do similar type of columns addition in Pyspark?

I want to do addition of similar type of columns (total columns are more than 100) as follows:
id
b
c
d
b_apac
c_apac
d_apac
abcd
3
5
null
45
9
1
bcd
13
15
1
45
2
10
cd
32
null
6
45
90
1
resultant table should look like this:
id
b_sum
c_sum
d_sum
abcd
48
14
1
bcd
58
17
11
cd
77
90
7
Please help me with some generic code as I have more than 100 columns to do this for. |

You can use use sum and check the prefix of your column name:
df.select(
'id',
sum([df[col] for col in df.columns if col.startswith('b')]).alias('b_sum'),
sum([df[col] for col in df.columns if col.startswith('c')]).alias('c_sum'),
sum([df[col] for col in df.columns if col.startswith('d')]).alias('d_sum'),
).show(10, False)

Grouping data based on month-year in pandas and then dropping all entries except the latest one- Python

Below is my example dataframe
Date Indicator Value
0 2000-01-30 A 30
1 2000-01-31 A 40
2 2000-03-30 C 50
3 2000-02-27 B 60
4 2000-02-28 B 70
5 2000-03-31 C 90
6 2000-03-28 C 100
7 2001-01-30 A 30
8 2001-01-31 A 40
9 2001-03-30 C 50
10 2001-02-27 B 60
11 2001-02-28 B 70
12 2001-03-31 C 90
13 2001-03-28 C 100
Desired Output
Date Indicator Value
2000-01-31 A 40
2000-02-28 B 70
2000-03-31 C 90
2001-01-31 A 40
2001-02-28 B 70
2001-03-31 C 90
I want to write a code that groups data by particular month-year and then keep the entry of latest date in that particular month-year and drop the rest. The data is till year 2020
I was only able to fetch the count by month-year. I am not able to drop create a proper code that helps to group data as per month-year and indicator and get the correct results

Use Series.dt.to_period for months periods, aggregate index of maximal date per groups by DataFrameGroupBy.idxmax and then pass to DataFrame.loc:
df['Date'] = pd.to_datetime(df['Date'])
print (df['Date'].dt.to_period('m'))
0 2000-01
1 2000-01
2 2000-03
3 2000-02
4 2000-02
5 2000-03
6 2000-03
7 2001-01
8 2001-01
9 2001-03
10 2001-02
11 2001-02
12 2001-03
13 2001-03
Name: Date, dtype: period[M]
df = df.loc[df.groupby(df['Date'].dt.to_period('m'))['Date'].idxmax()]
print (df)
Date Indicator Value
1 2000-01-31 A 40
4 2000-02-28 B 70
5 2000-03-31 C 90
8 2001-01-31 A 40
11 2001-02-28 B 70
12 2001-03-31 C 90

Read excel and reformat the multi-index headers in Pandas

Given a excel file with format as follows:
Reading with pd.read_clipboard, I get:
year 2018 Unnamed: 2 2019 Unnamed: 4
0 city quantity price quantity price
1 bj 10 2 4 7
2 sh 6 8 3 4
Just wondering if it's possible to convert to the following format with Pandas:
year city quantity price
0 2018 bj 10 2
1 2019 bj 4 7
2 2018 sh 6 8
3 2019 sh 3 4

I think here is best convert excel file to DataFrame with MultiIndex in columns and first column as index:
df = pd.read_excel(file, header=[0,1], index_col=[0])
print (df)
year 2018 2019
city quantity price quantity price
bj 10 2 4 7
sh 6 8 3 4
print (df.columns)
MultiIndex([('2018', 'quantity'),
('2018', 'price'),
('2019', 'quantity'),
('2019', 'price')],
names=['year', 'city'])
Then reshape by DataFrame.stack, change order of levels by DataFrame.swaplevel, set index and columns names by DataFrame.rename_axis and last convert index to columns, and if encessary convert year to integers:
df1 = (df.stack(0)
.swaplevel(0,1)
.rename_axis(index=['year','city'], columns=None)
.reset_index()
.assign(year=lambda x: x['year'].astype(int)))
print (df1)
year city price quantity
0 2018 bj 2 10
1 2019 bj 7 4
2 2018 sh 8 6
3 2019 sh 4 3

Pandas dataframe not correct format for groupby, what is wrong?

I am trying to sum all columns based on the value of the first, but groupby.sum is unexpectedly not working.
Here is a minimal example:
import pandas as pd
data = [['Alex',10, 11],['Bob',12, 10],['Clarke',13, 9], ['Clarke',1, 1]]
df = pd.DataFrame(data,columns=['Name','points1', 'points2'])
print(df)
df.groupby('Name').sum()
print(df)
I get this:
Name points1 points2
0 Alex 10 11
1 Bob 12 10
2 Clarke 13 9
3 Clarke 1 1
And not this:
Name points1 points2
0 Alex 10 11
1 Bob 12 10
2 Clarke 14 10
From what i understand, the dataframe is not the right format for pandas to perform group by. I would like to understand what is wrong with it because this is just a toy example but i have the same problem with a real data-set.
The real data i'm trying to read is the John Hopkins University Covid-19 dataset:
https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series

You forget assign output of aggregation to variable, because aggregation not working inplace. So in your solution print (df) before and after groupby returned same original DataFrame.
df1 = df.groupby('Name', as_index=False).sum()
print (df1)
Name points1 points2
0 Alex 10 11
1 Bob 12 10
2 Clarke 14 10
Or you can set to same variable df:
df = df.groupby('Name', as_index=False).sum()
print (df)
Name points1 points2
0 Alex 10 11
1 Bob 12 10
2 Clarke 14 10

pandas combine a data frame with another groupby dataframe

I have two data frames with structure as given below.
>>> df1
IID NAME TEXT
0 10 One AA,AB
1 11 Two AB,AC
2 12 Three AB
3 13 Four AC
>>> df2
IID TEXT
0 10 aa
1 10 ab
2 11 abc
3 11 a,c
4 11 ab
5 12 AA
6 13 AC
7 13 ad
8 13 abc
I want them to combine such that new data frame is a copy of df1 with the TEXT field appearing in df2 for the corresponding IID is appended to the TEXT field of df1 with duplicates removed (cases insensitive duplication check).
My expected output is
>>> df1
IID NAME TEXT
0 10 One AA,AB
1 11 Two AB,AC,ABC,A,C
2 12 Three AB,AA
3 13 Four AC,AD,ABC
I tried with groupby on df2, but how can I do the joint of the groupie object to a dataframe ?

I believe you need concat with groupby.agg to create the skeleton with duplicates , then series.explode with groupby+unique for de-duplicating
out = (pd.concat((df1,df2),sort=False).groupby('IID')
.agg({'NAME':'first','TEXT':','.join}).reset_index())
out['TEXT'] = (out['TEXT'].str.upper().str.split(',').explode()
.groupby(level=0).unique().str.join(','))
print(out)
IID NAME TEXT
0 10 One AA,AB
1 11 Two AB,AC,ABC,A,C
2 12 Three AB,AA
3 13 Four AC,AD,ABC

I took the reverse steps. First combined the rows having the same values to a list then merge and then combine the two columns into a single column.
df1:
IID NAME TEXT
0 10 One AA,AB
1 11 Two AB,AC
2 12 Three AB
3 13 Four AC
df2:
IID TEXT
0 10 aa
1 10 ab
2 11 abc
3 11 a,c
4 11 ab
5 12 AA
6 13 AC
7 13 ad
8 13 abc
df3 = pd.DataFrame(df2.groupby("IID")['TEXT'].apply(list).transform(lambda x: ','.join(x).upper()).reset_index())
df3:
IID TEXT
0 10 AA,AB
1 11 ABC,A,C,AB
2 12 AA
3 13 AC,AD,ABC
df4 = pd.merge(df1,df3,on='IID')
df4:
IID NAME TEXT_x TEXT_y
0 10 One AA,AB AA,AB
1 11 Two AB,AC ABC,A,C,AB
2 12 Three AB AA
3 13 Four AC AC,AD,ABC
df4['TEXT'] = df4[['TEXT_x','TEXT_y']].apply(
lambda x: ','.join(pd.unique(','.join(x).split(','))),
axis=1
)
df4.drop(['TEXT_x','TEXT_y'],axis=1)
OR
df5 = df1.assign(TEXT = df4.apply(
lambda x: ','.join(pd.unique(','.join(x[['TEXT_x','TEXT_y']]).split(','))),
axis=1))
df4/df5:
IID NAME TEXT
0 10 One AA,AB
1 11 Two AB,AC,ABC,A,C
2 12 Three AB,AA
3 13 Four AC,AD,ABC

Resources