I have dataframe1 with columns a,b,c,d with 5 rows.
I also have another dataframe2 with columns e,f,g,h
Let's say I want to copy columns a,b in row 3 from dataframe1 to columns f,g in row 3 at dataframe2.
I tried to use this code:
dataframe2.loc[3,['f','g']] = dataframe1.loc[3,['a','b']].
The results was NaN in dataframe2.
Any ideas how can I solve it?
One idea is convert to numpy array for avoid alignment data by columns names:
dataframe2.loc[3,['f','g']] = dataframe1.loc[3,['a','b']].values
Sample:
dataframe1 = pd.DataFrame({'a':list('abcdef'),
'b':[4,5,4,5,5,4],
'c':[7,8,9,4,2,3]})
print (dataframe1)
a b c
0 a 4 7
1 b 5 8
2 c 4 9
3 d 5 4
4 e 5 2
5 f 4 3
dataframe2 = pd.DataFrame({'f':list('HIJK'),
'g':[0,0,7,1],
'h':[0,1,0,1]})
print (dataframe2)
f g h
0 H 0 0
1 I 0 1
2 J 7 0
3 K 1 1
dataframe2.loc[3,['f','g']] = dataframe1.loc[3,['a','b']].values
print (dataframe2)
f g h
0 H 0 0
1 I 0 1
2 J 7 0
3 d 5 1
Related
I have a dataframe like below in which I need to replace the 0s with the mean of the rows where the parent_key matches the self_key.
Input DataFrame: df= pd.DataFrame ({'self_key':['a','b','c','d','e','e','e','f','f','f'],'parent_key':[np.nan,'a','b','b','c','c','c','d','d','d'], 'value':[0,0,0,0,4,6,14,12,8,22],'level':[1,2,3,3,4,4,4,4,4,4]})
The row 3 has self_key of 'd' so I would need to replace its 0 value in column 'value' with the mean of rows 7,8,9 to fill with the correct value of 14. Since the lower levels feed into the higher levels I would need to do it from lowest level to highest to fill out the dataframe as well but when I do the below code it doesn't work and I get the error "ValueError: Grouper for '<class 'pandas.core.frame.DataFrame'>' not 1-dimensional". How can I fill in the 0s with the means from lowest level to highest?
df['value']=np.where((df['value']==0) & (df['level']==3), df['value'].groupby(df.where(df['parent_key']==df['self_key'])).transform('mean'), df['value'])
Input
self_key parent_key value level
0 a NaN 0 1
1 b a 0 2
2 c b 0 3
3 d b 0 3
4 e c 4 4
5 e c 6 4
6 e c 14 4
7 f d 12 4
8 f d 8 4
9 f d 22 4
My approach is to repeat the above code 3 times and change the level from 3 to 2 to 1, but its not working for even level 3.
Expected Ouput:
self_key parent_key value level
0 a NaN 11 1
1 b a 11 2
2 c b 8 3
3 d b 14 3
4 e c 4 4
5 e c 6 4
6 e c 14 4
7 f d 12 4
8 f d 8 4
9 f d 22 4
If I understand your problem correctly, you are trying to compute mean in a bottom-up fashion by filtering dataframe on certain keys. If so, then following should solve it:
for l in range(df["level"].max()-1, 0, -1):
df_sub = df[(df["level"] == l) & (df["value"] == 0)]
self_keys = df_sub["self_key"].tolist()
for k in self_keys:
df.loc[df_sub[df_sub["self_key"] == k].index, "value"] = df[df["parent_key"] == k]["value"].mean()
[Out]:
self_key parent_key value level
0 a 11 1
1 b a 11 2
2 c b 8 3
3 d b 14 3
4 e c 4 4
5 e c 6 4
6 e c 14 4
7 f d 12 4
8 f d 8 4
9 f d 22 4
I have a dataframe as below.
My dataframe as below.
ID list
1 a, b, c
2 a, s
3 NA
5 f, j, l
I need to break each items in the list column(String) into independent row as below:
ID item
1 a
1 b
1 c
2 a
2 s
3 NA
5 f
5 j
5 l
Thanks.
Use str.split to separate your items then explode:
print (df.assign(list=df["list"].str.split(", ")).explode("list"))
ID list
0 1 a
0 1 b
0 1 c
1 2 a
1 2 s
2 3 NaN
3 5 f
3 5 j
3 5 l
A beginners approach : Just another way of doing the same thing using pd.DataFrame.stack
df['list'] = df['list'].map(lambda x : str(x).split(','))
dfOut = pd.DataFrame(df['list'].values.tolist())
dfOut.index = df['ID']
dfOut = dfOut.stack().reset_index()
del dfOut['level_1']
dfOut.rename(columns = {0 : 'list'}, inplace = True)
Output:
ID list
0 1 a
1 1 b
2 1 c
3 2 a
4 2 s
5 3 nan
6 5 f
7 5 j
8 5 l
I have the following DataFrame
Input:
A B C D E
2 3 4 5 6
1 1 2 3 2
2 3 4 5 6
I want to add a new column that has the minimum of A, B and C for that row
Output:
A B C D E Goal
2 3 4 5 6 2
1 1 2 3 2 1
2 3 4 5 6 2
I have tried to use
df = df[['A','B','C]].min()
but I get errors about hashing lists and also I think this will be the min of the whole column I only want the min of the row for those specific columns.
How can I best accomplish this?
Use min along the columns with axis=1
Inline solution that produces copy that doesn't alter the original
df.assign(Goal=lambda d: d[['A', 'B', 'C']].min(1))
A B C D E Goal
0 2 3 4 5 6 2
1 1 1 2 3 2 1
2 2 3 4 5 6 2
Same answer put different
Add column to existing dataframe
new = df[['A', 'B', 'C']].min(axis=1)
df['Goal'] = new
df
A B C D E Goal
0 2 3 4 5 6 2
1 1 1 2 3 2 1
2 2 3 4 5 6 2
Add axis = 1 to your min
df['Goal'] = df[['A','B','C']].min(axis = 1)
you have to define an axis across which you are applying the min function, which would be 1 (columns).
df['ABC_row_min'] = df[['A', 'B', 'C']].min(axis = 1)
I currently have a pandas dataframe where values between 0 and 1 are saved. I am looking for a function which can provide me the top 5 values of a column, together with the name of the column and the associated index of the values.
Sample Input: data frame with column names a:z, index 1:23, entries are values between 0 and 1
Sample Output: array of 5 highest entries in each column, each with column name and index
Edit:
For the following data frame:
np.random.seed([3,1415])
df = pd.DataFrame(np.random.randint(10, size=(10, 4)), list('abcdefghij'), list('ABCD'))
df
A B C D
a 0 2 7 3
b 8 7 0 6
c 8 6 0 2
d 0 4 9 7
e 3 2 4 3
f 3 6 7 7
g 4 5 3 7
h 5 9 8 7
i 6 4 7 6
j 2 6 6 5
I would like to get an output like (for example for the first column):
[[8,b,A], [8, c, A], [6,i,A], [5, h, A], [4,g,A]].
consider the dataframe df
np.random.seed([3,1415])
df = pd.DataFrame(
np.random.randint(10, size=(10, 4)), list('abcdefghij'), list('ABCD'))
df
A B C D
a 0 2 7 3
b 8 7 0 6
c 8 6 0 2
d 0 4 9 7
e 3 2 4 3
f 3 6 7 7
g 4 5 3 7
h 5 9 8 7
i 6 4 7 6
j 2 6 6 5
I'm going to use np.argpartition to separate each column into the 5 smallest and 10 - 5 (also 5) largest
v = df.values
i = df.index.values
k = len(v) - 5
pd.DataFrame(
i[v.argpartition(k, 0)[-k:]],
np.arange(k), df.columns
)
A B C D
0 g f i i
1 b c a d
2 h h f h
3 i b d f
4 c j h g
print(your_dataframe.sort_values(ascending=False)[0:4])
Given the following data frame:
import pandas as pd
df=pd.DataFrame({'A':['A','A','A','B','B','B'],
'B':[1,1,2,1,1,1],
'C':[2,4,6,3,5,7]})
df
A B C
0 A 1 2
1 A 1 4
2 A 2 6
3 B 1 3
4 B 1 5
5 B 1 7
Wherever there are duplicate rows per columns 'A' and 'B', I'd like to combine those rows and sum the value under column 'C' like this:
A B C
0 A 1 6
2 A 2 6
3 B 1 15
So far, I can at least identify the duplicates like this:
df['Dup']=df.duplicated(['A','B'],keep=False)
Thanks in advance!
use groupby() and sum():
In [94]: df.groupby(['A','B']).sum().reset_index()
Out[94]:
A B C
0 A 1 6
1 A 2 6
2 B 1 15