I have a data frame as follows,
Category Feature valueCount
A color 153
A color 7
A color 48
A color 16
B length 5
C height 1
C height 16
I want to get the sum of valueCount by Category and Feature
I am using the following code;
DF['valueSum'] = DF.groupby(['Category','Feature'])['valueCount'].transform('sum')
I am getting the output as;
Category Feature valueCount valueSum
A color 153 26018
A color 7 26018
A color 48 26018
A color 16 26018
B length 5 25
C height 1 257
C height 16 257
which is really weird, as it is taking the square of valueCount and then adding up. Anyone knows, what is going wrong here?
According to the doc, The GroupBy objects provides a sum method that do what you need:
In [12]: grouped.sum()
the ideal way is:
In [4]: df
Out[4]:
Category Feature valueCount
0 A color 153
1 A color 7
2 A color 48
3 A color 16
4 B length 5
5 C height 1
6 C height 16
In [5]: df.groupby(df['Category']).sum()
Out[5]:
valueCount
Category
A 224
B 5
C 17
Related
When trying to solve my own question here I came up with an interesting problem. Consider I have this dataframe
import pandas as pd
import numpy as np
np.random.seed(0)
df= pd.DataFrame(dict(group = np.random.choice(["a", "b", "c", "d"],
size = 100),
values = np.random.randint(0, 100,
size = 100)
)
)
I want to select top values per each group, but I want to select the values according to some range. Let's say, top x to y values per each group. If any group has less than x values in it, give top(min((y-x), x)) values for that group.
In general, I am looking for a custom made alternative function which could be used with groupby objects to select not top n values, but instead top x to y range of values.
EDIT: nlargest() is a special case of the solution to my problem where x = 1 and y = n
Any further help, or guidance will be appreciated
Adding an example with this df and this top(3, 6). For every group output the values from top 3rd until top 6th values:
group value
a 190
b 166
a 163
a 106
b 86
a 77
b 70
b 69
c 67
b 54
b 52
a 50
c 24
a 20
a 11
As group c has just two members, it will output top(3)
group value
a 106
a 77
a 50
b 69
b 54
b 52
c 67
c 24
there are other means of doing this and depending on how large your dataframe is, you may want to search groupby slice or something similar. You may also need to check my conditions are correct (<, <=, etc)
x=3
y=6
# this gets the groups which don't meet the x minimum
df1 = df[df.groupby('group')['value'].transform('count')<x]
# this df takes those groups meeting the minimum and then shifts by x-1; does some cleanup and chooses nlargest
df2 = df[df.groupby('group')['value'].transform('count')>=x].copy()
df2['shifted'] = df2.groupby('group').shift(periods=-(x-1))
df2.drop('value', axis=1, inplace=True)
df2 = df2.groupby('group')['shifted'].nlargest(y-x).reset_index().rename(columns={'shifted':'value'}).drop('level_1', axis=1)
# putting it all together
df_final = pd.concat([df1, df2])
df_final
group value
8 c 67.0
12 c 24.0
0 a 106.0
1 a 77.0
2 a 50.0
3 b 70.0
4 b 69.0
5 b 54.0
This is my pandas df:
Id Protein A_Egg B_Meat C_Milk Category
A 10 10 20 0 egg
B 20 10 0 10 milk
C 20 10 10 10 meat
D 25 20 10 0 egg
I wish to merge protein column with other column based on "Category"
My output is
Id Protein_final
A 20
B 30
C 30
D 45
Ideally, I would like to show how I am approaching but, I am frankly clueless!!
EDIT: Also, How to handle is the category is blank or does meet one of the column (in that can final should be same as initial value in protein column)
Use DataFrame.lookup with some preprocessing with remove values in columns names before _ and lowercase, last add to column:
arr = df.rename(columns=lambda x: x.split('_')[-1].lower()).lookup(df.index, df['Category'])
df['Protein'] += arr
print (df)
Id Protein A_Egg B_Meat C_Milk Category
0 A 20 10 20 0 egg
1 B 30 10 0 10 milk
2 C 30 10 10 10 meat
3 D 45 20 10 0 egg
If need only 2 columns finally:
df = df[['Id','Protein']]
You can melt the dataframe, and filter for rows where category equals the variable column, and sum the final columns :
(
df
.melt(["Id", "Protein", "Category"])
.assign(variable=lambda x: x.variable.str[2:].str.lower(),
Protein_final=lambda x: x.Protein + x.value)
.query("Category == variable")
.filter(["Id", "Protein_final"])
)
Id Protein_final
0 A 20
3 D 45
6 C 30
9 B 30
I have a dataframe and want to use pct_chg method to calculate the % change between only 2 of the selected columns, B and C, and put the output into a new column. the below code doesnt seem to work. can anyone help me?
df2 = pd.DataFrame(np.random.randint(0,50,size=(100, 4)), columns=list('ABCD'))
df2['new'] = df2.pct_change(axis=1)['B']['C']
Try:
df2['new'] = df2[['B','C']].pct_change(axis=1)['C']
pct_change returns pct_change across all the columns, you can select the required column and assign to a new variable.
df2['new'] = df2.pct_change(axis=1)['C']
A B C D new
0 29 4 29 5 6.250000
1 14 35 2 40 -0.942857
2 5 18 31 10 0.722222
3 17 10 42 41 3.200000
4 24 48 47 35 -0.020833
IIUC, you can just do the following:
df2['new'] = (df2['C']-df2['B'])/df2['B']
I would like to apply a custom function to each level within a multiindex.
For example, I have the dataframe
df = pd.DataFrame(np.arange(16).reshape((4,4)),
columns=pd.MultiIndex.from_product([['OP','PK'],['PRICE','QTY']]))
of which I want to add a column for each level 0 column, called "Value" which is the result of the following function;
def my_func(df, scale):
return df['QTY']*df['PRICE']*scale
where the user supplies the "scale" value.
Even in setting up this example, I am not sure how to show the result I want. But I know I want the final dataframe's multiindex column to be
pd.DataFrame(columns=pd.MultiIndex.from_product([['OP','PK'],['PRICE','QTY','Value']]))
Even if that wasn't had enough, I want to apply one "scale" value for the "OP" level 0 column and a different "scale" value to the "PK" column.
Use:
def my_func(df, scale):
#select second level of columns
df1 = df.xs('QTY', axis=1, level=1).values *df.xs('PRICE', axis=1, level=1) * scale
#create MultiIndex in columns
df1.columns = pd.MultiIndex.from_product([df1.columns, ['val']])
#join to original
return pd.concat([df, df1], axis=1).sort_index(axis=1)
print (my_func(df, 10))
OP PK
PRICE QTY val PRICE QTY val
0 0 1 0 2 3 60
1 4 5 200 6 7 420
2 8 9 720 10 11 1100
3 12 13 1560 14 15 2100
EDIT:
For multiple by scaled values different for each level is possible use list of values:
print (my_func(df, [10, 20]))
OP PK
PRICE QTY val PRICE QTY val
0 0 1 0 2 3 120
1 4 5 200 6 7 840
2 8 9 720 10 11 2200
3 12 13 1560 14 15 4200
Use groupby + agg, and then concatenate the pieces together with pd.concat.
scale = 10
v = df.groupby(level=0, axis=1).agg(lambda x: x.values.prod(1) * scale)
v.columns = pd.MultiIndex.from_product([v.columns, ['value']])
pd.concat([df, v], axis=1).sort_index(axis=1, level=0)
OP PK
PRICE QTY value PRICE QTY value
0 0 1 0 2 3 60
1 4 5 200 6 7 420
2 8 9 720 10 11 1100
3 12 13 1560 14 15 2100
First, let me set the stage.
I start with a pandas dataframe klmn, that looks like this:
In [15]: klmn
Out[15]:
K L M N
0 0 a -1.374201 35
1 0 b 1.415697 29
2 0 a 0.233841 18
3 0 b 1.550599 30
4 0 a -0.178370 63
5 0 b -1.235956 42
6 0 a 0.088046 2
7 0 b 0.074238 84
8 1 a 0.469924 44
9 1 b 1.231064 68
10 2 a -0.979462 73
11 2 b 0.322454 97
Next I split klmn into two dataframes, klmn0 and klmn1, according to the value in the 'K' column:
In [16]: k0 = klmn.groupby(klmn['K'] == 0)
In [17]: klmn0, klmn1 = [klmn.ix[k0.indices[tf]] for tf in (True, False)]
In [18]: klmn0, klmn1
Out[18]:
( K L M N
0 0 a -1.374201 35
1 0 b 1.415697 29
2 0 a 0.233841 18
3 0 b 1.550599 30
4 0 a -0.178370 63
5 0 b -1.235956 42
6 0 a 0.088046 2
7 0 b 0.074238 84,
K L M N
8 1 a 0.469924 44
9 1 b 1.231064 68
10 2 a -0.979462 73
11 2 b 0.322454 97)
Finally, I compute the mean of the M column in klmn0, grouped by the value in the L column:
In [19]: m0 = klmn0.groupby('L')['M'].mean(); m0
Out[19]:
L
a -0.307671
b 0.451144
Name: M
Now, my question is, how can I subtract m0 from the M column of the klmn1 sub-dataframe, respecting the value in the L column? (By this I mean that m0['a'] gets subtracted from the M column of each row in klmn1 that has 'a' in the L column, and likewise for m0['b'].)
One could imagine doing this in a way that replaces the the values in the M column of klmn1 with the new values (after subtracting the value from m0). Alternatively, one could imagine doing this in a way that leaves klmn1 unchanged, and instead produces a new dataframe klmn11 with an updated M column. I'm interested in both approaches.
If you reset the index of your klmn1 dataframe to be that of the column L, then your dataframe will automatically align the indices with any series you subtract from it:
In [1]: klmn1.set_index('L')['M'] - m0
Out[1]:
L
a 0.777595
a -0.671791
b 0.779920
b -0.128690
Name: M
Option #1:
df1.subtract(df2, fill_value=0)
Option #2:
df1.subtract(df2, fill_value=None)