i want to replace all rows that have "A" in name column
with single row from another df
i got this
data={"col1":[2,3,4,5,7],
"col2":[4,2,4,6,4],
"col3":[7,6,9,11,2],
"col4":[14,11,22,8,5],
"name":["A","A","V","A","B"],
"n_roll":[8,2,1,3,9]}
df=pd.DataFrame.from_dict(data)
df
that is my single row (the another df)
data2={"col1":[0]
,"col2":[1]
,"col3":[5]
,"col4":[6]
}
df2=pd.DataFrame.from_dict(data2)
df2
that how i want it to look like
data={"col1":[0,0,4,0,7],
"col2":[1,1,4,1,4],
"col3":[5,5,9,5,2],
"col4":[6,6,22,6,5],
"name":["A","A","V","A","B"],
"n_roll":[8,2,1,3,9]}
df=pd.DataFrame.from_dict(data)
df
i try do this df.loc[df["name"]=="A"][df2.columns]=df2
but it did not work
We can try mask + combine_first
df = df.mask(df['name'].eq('A'), df2.loc[0], axis=1).combine_first(df)
df
col1 col2 col3 col4 name n_roll
0 0 1 5 6 A 8.0
1 0 1 5 6 A 2.0
2 4 4 9 22 V 1.0
3 0 1 5 6 A 3.0
4 7 4 2 5 B 9.0
df.loc[df["name"]=="A"][df2.columns]=df2 is index-chaining and is not expected to work. For details, see the doc.
You can also use boolean indexing like this:
df.loc[df['name']=='A', df2.columns] = df2.values
Output:
col1 col2 col3 col4 name n_roll
0 0 1 5 6 A 8
1 0 1 5 6 A 2
2 4 4 9 22 V 1
3 0 1 5 6 A 3
4 7 4 2 5 B 9
Related
I am creating a tool to automate some tasks. These tasks generate two DataFrames, but when concatenating them the columns are messed up as follows:
col2 col4 col3 col1
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
But I need to rearrange them so that they look like this:
col1 col2 col3 col4
0 a A 0 2
1 B A 1 1
2 c B 9 9
3 D NaN 4 8
4 e D 2 7
5 F C 3 4
Can someone help me?
I tried with sort_values, but it didn't work, and I can't find anywhere another way to try to solve the problem.
use following code:
df.sort_index(axis=1)
You can do:
df = df[sorted(df.columns.tolist())].copy()
df = df[['col1', 'col2', 'col3', 'col4']]
Assume, I have a data frame such as
import pandas as pd
df = pd.DataFrame({'visitor':['A','B','C','D','E'],
'col1':[1,2,3,4,5],
'col2':[1,2,4,7,8],
'col3':[4,2,3,6,1]})
visitor
col1
col2
col3
A
1
1
4
B
2
2
2
C
3
4
3
D
4
7
6
E
5
8
1
For each row/visitor, (1) First, if there are any identical values, I would like to keep the 1st value of each row then replace the rest of identical values in the same row with NULL such as
visitor
col1
col2
col3
A
1
NULL
4
B
2
NULL
NULL
C
3
4
NULL
D
4
7
6
E
5
8
1
Then (2) keep rows/visitors with more than 1 value such as
Final Data Frame
visitor
col1
col2
col3
A
1
NULL
4
C
3
4
NULL
D
4
7
6
E
5
8
1
Any suggestions? many thanks
We can use series.duplicated along the columns axis to identify the duplicates, then mask the duplicates using where and filter the rows where the sum of non-duplicated values is greater than 1
s = df.set_index('visitor')
m = ~s.apply(pd.Series.duplicated, axis=1)
s.where(m)[m.sum(1).gt(1)]
col1 col2 col3
visitor
A 1 NaN 4.0
C 3 4.0 NaN
D 4 7.0 6.0
E 5 8.0 1.0
Let us try mask with pd.Series.duplicated, then dropna with thresh
out = df.mask(df.apply(pd.Series.duplicated,1)).dropna(thresh = df.shape[1]-1)
Out[321]:
visitor col1 col2 col3
0 A 1 NaN 4.0
2 C 3 4.0 NaN
3 D 4 7.0 6.0
4 E 5 8.0 1.0
I have a pandas DataFrame that looks similar to the following...
>>> df = pd.DataFrame({
... 'col1':['A','C','B','A','B','C','A'],
... 'col2':[np.nan,1.,np.nan,1.,1.,np.nan,np.nan],
... 'col3':[0,1,9,4,2,3,5],
... })
>>> df
col1 col2 col3
0 A NaN 0
1 C 1.0 1
2 B NaN 9
3 A 1.0 4
4 B 1.0 2
5 C NaN 3
6 A NaN 5
What I would like to do is group the rows of col1 by value and then update any NaN values in col2 to increment in value by 1 based on the last highest value of that group in col1.
So that my expected results would look like the following...
>>> df
col1 col2 col3
0 A 1.0 4
1 A 2.0 0
2 A 3.0 5
3 B 1.0 2
4 B 2.0 9
5 C 1.0 1
6 C 2.0 3
I believe I can use something like groupby on col1 though I'm unsure how to increment the value in col2 based on the last highest value of the group from col1. I've tried the following, but instead of incrementing the value of col1 it updates the value to all 1.0 and adds an additional column...
>>> df1 = df.groupby(['col1'], as_index=False).agg({'col2': 'min'})
>>> df = pd.merge(df1, df, how='left', left_on=['col1'], right_on=['col1'])
>>> df
col1 col2_x col2_y col3
0 A 1.0 NaN 0
1 A 1.0 1.0 1
2 A 1.0 NaN 5
3 B 1.0 NaN 9
4 B 1.0 1.0 4
5 C 1.0 1.0 2
6 C 1.0 NaN 3
Use GroupBy.cumcount only for rows with missing values, add maximum value per group with GroupBy.transform and max and last replace by original values by fillna:
df = pd.DataFrame({
'col1':['A','C','B','A','B','B','B'],
'col2':[np.nan,1.,np.nan,1.,3.,np.nan, 0],
'col3':[0,1,9,4,2,3,4],
})
print (df)
col1 col2 col3
0 A NaN 0
1 C 1.0 1
2 B NaN 9
3 A 1.0 4
4 B 3.0 2
5 B NaN 3
6 B 0.0 4
df = df.sort_values(['col1','col2'], na_position='last')
s = df.groupby('col1')['col2'].transform('max')
df['new'] = (df[df['col2'].isna()]
.groupby('col1')
.cumcount()
.add(1)
.add(s)
.fillna(df['col2']).astype(int))
print (df)
col1 col2 col3 new
3 A 1.0 4 1
0 A NaN 0 2
6 B 0.0 4 0
4 B 3.0 2 3
2 B NaN 9 4
5 B NaN 3 5
1 C 1.0 1 1
Another way:
df['col2_new'] = df.groupby('col1')['col2'].apply(lambda x: x.replace(np.nan, x.value_counts().index[0]+1))
df = df.sort_values('col1')
Let's say I have a dataframe (I'll just use a simple example) that looks like this:
import pandas as pd
df = {'Col1':[3,4,2,6,5,7,3,4,9,7,1,3],
'Col2':['B','B','B','B','A','A','A','A','C','C','C','C',],
'Col3':[1,1,2,2,1,1,2,2,1,1,2,2]}
df = pd.DataFrame(df)
Which gives a dataframe like so:
Col1 Col2 Col3
0 3 B 1
1 4 B 1
2 2 B 2
3 6 B 2
4 5 A 1
5 7 A 1
6 3 A 2
7 4 A 2
8 9 C 1
9 7 C 1
10 1 C 2
11 3 C 2
What I want to do is several steps:
1) For each unique value in Col2, and for each unique value in Col3, average Col1. So a desired output would be:
Avg Col2 Col3
1 3.5 B 1
2 4 B 2
3 6 A 1
4 3.5 A 2
5 8 C 1
6 2 C 2
2) Now, for each unique value in Col3, I want the highest average and the corresponding value in Col2. So
Best Avg Col2 Col3
1 8 C 1
2 4 B 2
My attempt has been using df.groupby(['Col3','Col2'], as_index = False).agg({'Col1':'mean'}).groupby(['Col3']).agg({'Col1':'max'})
This gives me the highest average for each Col3 value, but not the corresponding Col2 label. Thank you for any help you can give!
After you first groupby do sort_values + drop_duplicates
g1=df.groupby(['Col3','Col2'], as_index = False).agg({'Col1':'mean'})
g1.sort_values('Col1').drop_duplicates('Col3',keep='last')
Out[569]:
Col3 Col2 Col1
4 2 B 4.0
2 1 C 8.0
Or in case you have duplicate max value of mean
g1[g1.Col1==g1.groupby('Col3').Col1.transform('max')]
Do the following (I modified your code slightly,
to make it a bit shorter):
df2 = df.groupby(['Col3','Col2'], as_index = False).mean()
When you print the result, for your input, you will get:
Col3 Col2 Col1
0 1 A 6.0
1 1 B 3.5
2 1 C 8.0
3 2 A 3.5
4 2 B 4.0
5 2 C 2.0
Then run:
res = df2.iloc[df2.groupby('Col3').Col1.idxmax()]
When you print the result, you will get:
Col3 Col2 Col1
2 1 C 8.0
4 2 B 4.0
As you can see:
idxmax gives the index of the row with "maximal" element (for each
group),
this result you can use as the argument of iloc.
Working with a Pandas DataFrame, I am trying to copy data from one cell into another cell only if the recipient cell contains a specific value. The transfer should go from:
Col1 Col2
0 4 X
1 2 5
2 1 X
3 7 8
4 12 20
5 3 X
And the result should be
Col1 Col2
0 4 4
1 2 5
2 1 1
3 7 8
4 12 20
5 3 3
Is there an elegant or simple solution I am missing?
df.Col2 = df.Col1.where(df.Col2 == 'X', df.Col2)
import pandas as pd
import numpy as np
df.Col2 = np.where(df.Col2 == 'specific value', df.Col1, df.Col2)
Using pandas.DataFrame.ffill:
>>> df.replace('X', np.nan, inplace=True)
>>> df.ffill(axis=1)
Col1 Col2
0 4 4
1 2 5
2 1 1
3 7 8
4 12 20
5 3 3