I am using the split-apply-combine pattern in pandas to group my df by a custom aggregation function.
But this returns an undesired DataFrame with the grouped column existing twice: In an MultiIndex and the columns.
The following is a simplified example of my problem.
Say, I have this df
df = pd.DataFrame([[1,2],[3,4],[1,5]], columns=['A','B']))
A B
0 1 2
1 3 4
2 1 5
I want to group by column A and keep only those rows where B has an even value. Thus the desired df is this:
B
A
1 2
3 4
The custom function my_combine_func should do the filtering. But applying it after a groupby, leads to an MultiIndex with the former Index in the second level. And thus column A existing two times.
my_combine_func = group[group['B'] % 2 == 0]
df.groupby(['A']).apply(my_combine_func)
A B
A
1 0 1 2
3 1 3 4
How to apply a custom group function and have the desired df?
It's easier to use apply here so you get a boolean array back:
df[df.groupby('A')['B'].apply(lambda x: x % 2 == 0)]
A B
0 1 2
1 3 4
I have 3 data frame:
df1
id,k,a,b,c
1,2,1,5,1
2,3,0,1,0
3,6,1,1,0
4,1,0,5,0
5,1,1,5,0
df2
name,a,b,c
p,4,6,8
q,1,2,3
df3
type,w_ave,vac,yak
n,3,5,6
v,2,1,4
from the multiplication, using pandas and numpy, I want to the output in df1:
id,k,a,b,c,w_ave,vac,yak
1,2,1,5,1,16,15,18
2,3,0,1,0,0,3,6
3,6,1,1,0,5,4,7
4,1,0,5,0,0,11,14
5,1,1,5,0,13,12,15
the conditions are:
The value of the new column will be =
#its not a code
df1["w_ave"][1] = df3["w_ave"]["v"]+ df1["a"][1]*df2["a"]["q"]+df1["b"][1]*df2["b"]["q"]+df1["c"][1]*df2["c"]["q"]
for output["w_ave"][1]= 2 +(1*1)+(5*2)+(1*3)
df3["w_ave"]["v"]=2
df1["a"][1]=1, df2["a"]["q"]=1 ;
df1["b"][1]=5, df2["b"]["q"]=2 ;
df1["c"][1]=1, df2["c"]["q"]=3 ;
Which means:
- a new column will be added in df1, from the name of the column from df3.
- for each row of the df1, the value of a, b, c will be multiplied with the same-named q value from df2. and summed together with the corresponding value of df3.
-the column name of df1 , matched will column name of df2 will be multiplied. The other not matched column will not be multiplied, like df1[k].
- However, if there is any 0 in df1["a"], the corresponding output will be zero.
I am struggling with this. It was tough to explain also. My attempts are very silly. I know this attempt will not work. However, I have added this:
import pandas as pd, numpy as np
data1 = "Sample_data1.csv"
data2 = "Sample_data2.csv"
data3 = "Sample_data3.csv"
folder = '~Sample_data/'
df1 =pd.read_csv(folder + data1)
df2 =pd.read_csv(folder + data2)
df3 =pd.read_csv(folder + data3)
df1= df2 * df1
Ok, so this will in no way resemble your desired output, but vectorizing the formula you provided:
df2=df2.set_index("name")
df3=df3.set_index("type")
df1["w_ave"] = df3.loc["v", "w_ave"]+ df1["a"].mul(df2.loc["q", "a"])+df1["b"].mul(df2.loc["q", "b"])+df1["c"].mul(df2.loc["q", "c"])
Outputs:
id k a b c w_ave
0 1 2 1 5 1 16
1 2 3 0 1 0 4
2 3 6 1 1 0 5
3 4 1 0 5 0 12
4 5 1 1 5 0 13
I have pandas data frame have two-level group based on 'col10' and 'col1'.All I want to do is, drop all group rows if a specified value in another column repeated or this value did not existed in the group (keep the group which the specified value existed once only) for example:
The original data frame:
df = pd.DataFrame( {'col0':['A','A','A','A','A','B','B','B','B','B','B','B','c'],'col1':[1,1,2,2,2,1,1,1,1,2,2,2,1], 'col2':[1,2,1,2,3,1,2,1,2,2,2,2,1]})
I need to keep the the rows for the group for example (['A',1],['A',2],['B',2]) in this original DF
The desired dataframe:
I tried this step:
df.groupby(['col0','col1']).apply(lambda x: (x['col2']==1).sum()==1)
where the result is
col0 col1
A 1 True
2 True
B 1 False
2 True
c 1 False
dtype: bool
How to create the desired Df based on this bool?
You can do this as below:
m=(df.groupby(['col0','col1'])['col2'].
transform(lambda x: np.where((x.eq(1)).sum()==1,x,np.nan)).dropna().index)
df.loc[m]
Or:
df[df.groupby(['col0','col1'])['col2'].transform(lambda x: x.eq(1).sum()==1)]
col0 col1 col2
0 A 1 1
1 A 1 2
2 A 2 1
3 A 2 2
4 A 2 3
12 c 1 1
I have a dataframe
A B C
1 2 3
2 3 4
3 8 7
I want to take only rows where there is a sequence of 3,4 in columns C (in this scenario - first two rows)
What will be the best way to do so?
You can use rolling for general solution working with any pattern:
pat = np.asarray([3,4])
N = len(pat)
mask= (df['C'].rolling(window=N , min_periods=N)
.apply(lambda x: (x==pat).all(), raw=True)
.mask(lambda x: x == 0)
.bfill(limit=N-1)
.fillna(0)
.astype(bool))
df = df[mask]
print (df)
A B C
0 1 2 3
1 2 3 4
Explanation:
use rolling.apply and test pattern
replace 0s to NaNs by mask
use bfill with limit for filling first NANs values by last previous one
fillna NaNs to 0
last cast to bool by astype
Use shift
In [1085]: s = df.eq(3).any(1) & df.shift(-1).eq(4).any(1)
In [1086]: df[s | s.shift()]
Out[1086]:
A B C
0 1 2 3
1 2 3 4
We can use .idxmax to get the maximum value of a dataframe(df). My problem is that I have a df with several columns (more than 10), one of a column has identifiers of same value. I need to extract the identifiers with the maximum value:
>df
id value
a 0
b 1
b 1
c 0
c 2
c 1
Now, this is what I'd want:
>df
id value
a 0
b 1
c 2
I am trying to get it by using df.groupy(['id']), but it is a bit tricky:
df.groupby(["id"]).ix[df['value'].idxmax()]
Of course, that doesn't work. I fear that I am not on the right path, so I thought I'd ask you guys! Thanks!
Close! Groupby the id, then use the value column; return the max for each group.
In [14]: df.groupby('id')['value'].max()
Out[14]:
id
a 0
b 1
c 2
Name: value, dtype: int64
Op wants to provide these locations back to the frame, just create a transform and assign.
In [17]: df['max'] = df.groupby('id')['value'].transform(lambda x: x.max())
In [18]: df
Out[18]:
id value max
0 a 0 0
1 b 1 1
2 b 1 1
3 c 0 2
4 c 2 2
5 c 1 2