I have a data frame in pandas that looks like the following:
df =
Image_Number Parent_Object Child_Object
1 1 1
1 1 2
1 1 3
1 1 4
1 2 5
1 1 6
1 2 7
1 2 8
1 3 9
1 3 10
1 3 11
1 3 12
2 1 13
2 1 14
2 1 15
2 1 16
2 2 17
2 2 18
2 2 19
2 3 20
2 3 21
2 3 22
2 2 23
2 3 24
2 3 25
3 1 26
3 1 27
3 1 28
3 2 29
3 2 30
How could I write something that would classify the child objects to the parent objects for each image?
It would be extremely helpful to get an output like the following:
Image_Number Parent_Object Number_of_Child_Objects
1 1 5
1 2 3
1 3 4
2 1 4
2 2 3
2 3 3
3 1 3
3 2 2
What you want to do is calculate something (counts) for different values (groups) of Image_Number and Parent_Object. This can be done with the groupby method (see here for the docs: http://pandas.pydata.org/pandas-docs/stable/groupby.html
In your case:
df.groupby(by=['Image_Number', 'Parent_Object']).count()
Related
assume i have df:
pd.DataFrame({'data': [0,0,0,1,1,1,2,2,2,3,3,4,4,5,5,0,0,0,0,2,2,2,2,4,4,4,4]})
data
0 0
1 0
2 0
3 1
4 1
5 1
6 2
7 2
8 2
9 3
10 3
11 4
12 4
13 5
14 5
15 0
16 0
17 0
18 0
19 2
20 2
21 2
22 2
23 4
24 4
25 4
26 4
I'm looking for a way to create a new column in df that shows the number of data items repeated in new column For example:
data new
0 0 1
1 0 2
2 0 3
3 1 1
4 1 2
5 1 3
6 2 1
7 2 2
8 2 3
9 3 1
10 3 2
11 4 1
12 4 2
13 5 1
14 5 2
15 0 1
16 0 2
17 0 3
18 0 4
19 2 1
20 2 2
21 2 3
22 2 4
23 4 1
24 4 2
25 4 3
26 4 4
My logic was to get the rows to python list compare and create a new list.
Is there a simple way to do this?
Example
df = pd.DataFrame({'data': [0,0,0,1,1,1,2,2,2,3,3,4,4,5,5,0,0,0,0,2,2,2,2,4,4,4,4]})
Code
grouper = df['data'].ne(df['data'].shift(1)).cumsum()
df['new'] = df.groupby(grouper).cumcount().add(1)
df
data new
0 0 1
1 0 2
2 0 3
3 1 1
4 1 2
5 1 3
6 2 1
7 2 2
8 2 3
9 3 1
10 3 2
11 4 1
12 4 2
13 5 1
14 5 2
15 0 1
16 0 2
17 0 3
18 0 4
19 2 1
20 2 2
21 2 3
22 2 4
23 4 1
24 4 2
25 4 3
26 4 4
I am trying to conduct a mixed model analysis but would like to only include individuals who have data in all timepoints available. Here is an example of what my dataframe looks like:
import pandas as pd
ids = [1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,4,4,4,4,4,4]
timepoint = [1,2,3,4,5,6,1,2,3,4,5,6,1,2,4,1,2,3,4,5,6]
outcome = [2,3,4,5,6,7,3,4,1,2,3,4,5,4,5,8,4,5,6,2,3]
df = pd.DataFrame({'id':ids,
'timepoint':timepoint,
'outcome':outcome})
print(df)
id timepoint outcome
0 1 1 2
1 1 2 3
2 1 3 4
3 1 4 5
4 1 5 6
5 1 6 7
6 2 1 3
7 2 2 4
8 2 3 1
9 2 4 2
10 2 5 3
11 2 6 4
12 3 1 5
13 3 2 4
14 3 4 5
15 4 1 8
16 4 2 4
17 4 3 5
18 4 4 6
19 4 5 2
20 4 6 3
I want to only keep individuals in the id column who have all 6 timepoints. I.e. IDs 1, 2, and 4 (and cut out all of ID 3's data).
Here's the ideal output:
id timepoint outcome
0 1 1 2
1 1 2 3
2 1 3 4
3 1 4 5
4 1 5 6
5 1 6 7
6 2 1 3
7 2 2 4
8 2 3 1
9 2 4 2
10 2 5 3
11 2 6 4
12 4 1 8
13 4 2 4
14 4 3 5
15 4 4 6
16 4 5 2
17 4 6 3
Any help much appreciated.
You can count the number of unique timepoints you have, and then filter your dataframe accordingly with transform('nunique') and loc keeping only the ID's that contain all 6 of them:
t = len(set(timepoint))
res = df.loc[df.groupby('id')['timepoint'].transform('nunique').eq(t)]
Prints:
id timepoint outcome
0 1 1 2
1 1 2 3
2 1 3 4
3 1 4 5
4 1 5 6
5 1 6 7
6 2 1 3
7 2 2 4
8 2 3 1
9 2 4 2
10 2 5 3
11 2 6 4
15 4 1 8
16 4 2 4
17 4 3 5
18 4 4 6
19 4 5 2
20 4 6 3
Good morning
I have a current problem when trying to replace some values. I have a dataframe that has a column "loc10p" that separates the records into 10 groups, and for each group I have grouped those records into smaller groups, but each group has a starting range of 1 of the subgroups instead of counting the last subgroup. For example:
c2[c2.loc10p.isin([1,2])].sort_values(['loc10p','subgrupoloc10'])[['loc10p','subgrupoloc10']]
loc10p subgrupoloc10
1 1 1
7 1 1
15 1 1
0 1 2
14 1 2
30 1 2
31 1 2
2 2 1
8 2 1
9 2 1
16 2 1
17 2 1
18 2 2
23 2 2
How can I transform that into something like the following:
loc10p subgrupoloc10
1 1 1
7 1 1
15 1 1
0 1 2
14 1 2
30 1 2
31 1 2
2 2 3
8 2 3
9 2 3
16 2 3
17 2 3
18 2 4
23 2 4
I tried to do a loop that separates each group category into a different dataframe and then, replacing the values of the subgroup with a counter of the previous group, but it didn't replace anything:
w=1
temporal=[]
for e in range(1,11):
temp=c2[c2['loc10p']==e]
temporal.append(temp)
for e,i in zip(temporal,range(1,9)):
try:
e.loc[,'subgrupoloc10']=w
w+=1
except:
pass
Any help will be really appreciated!!
Try with ngroup
df['out'] = df.groupby(['loc10p','subgrupoloc10']).ngroup()+1
Out[204]:
1 1
7 1
15 1
0 2
14 2
30 2
31 2
2 3
8 3
9 3
16 3
17 3
18 4
23 4
dtype: int64
Try:
groups = (df["subgrupoloc10"] != df["subgrupoloc10"].shift()).cumsum()
df["subgrupoloc10"] = groups
print(df)
Prints:
loc10p subgrupoloc10
1 1 1
7 1 1
15 1 1
0 1 2
14 1 2
30 1 2
31 1 2
2 2 3
8 2 3
9 2 3
16 2 3
17 2 3
18 2 4
23 2 4
I have a pandas data frame which looks like below:
ID Value
1 2
2 6
3 3
4 5
I want a new dataframe which gives
ID Value
1 0
1 1
1 2
2 0
2 1
2 2
2 3
2 4
2 5
2 6
3 1
3 2
3 3
3 4
Any kind of suggestions would be appreciated.
Using reindex with repeat and cumcount for get the new value updated
df.reindex(df.index.repeat(df.Value+1)).assign(Value=lambda x : x.groupby('ID').cumcount())
Out[611]:
ID Value
0 1 0
0 1 1
0 1 2
1 2 0
1 2 1
1 2 2
1 2 3
1 2 4
1 2 5
1 2 6
2 3 0
2 3 1
2 3 2
2 3 3
3 4 0
3 4 1
3 4 2
3 4 3
3 4 4
3 4 5
Try,
new_df = df.groupby('ID').Value.apply(lambda x: pd.Series(np.arange(x+1)))\
.reset_index().drop('level_1', 1)
ID Value
0 1 0
1 1 1
2 1 2
3 2 0
4 2 1
5 2 2
6 2 3
7 2 4
8 2 5
9 2 6
10 3 0
11 3 1
12 3 2
13 3 3
14 4 0
15 4 1
16 4 2
17 4 3
18 4 4
19 4 5
Using stack and a list comprehension:
vals = [np.arange(i+1) for i in df.Value]
(pd.DataFrame(vals, index=df.ID)
.stack().reset_index(1, drop=True).astype(int).to_frame('Value'))
Value
ID
1 0
1 1
1 2
2 0
2 1
2 2
2 3
2 4
2 5
2 6
3 0
3 1
3 2
3 3
4 0
4 1
4 2
4 3
4 4
4 5
I'm figuring out how to assign a categorization from an increasing enumeration column. Here an example of my dataframe:
df = pd.DataFrame({'A':[1,1,1,1,1,1,2,2,3,3,3,3,3],'B':[1,2,3,12,13,14,1,2,5,6,7,8,50]})
This produce:
df
Out[9]:
A B
0 1 1
1 1 2
2 1 3
3 1 12
4 1 13
5 1 14
6 2 1
7 2 2
8 3 5
9 3 6
10 3 7
11 3 8
12 3 50
The column B has an increasing numerical serie, but sometimes the series is interrupted and keeps going with other numbers or start again. My desired output is:
Out[11]:
A B C
0 1 1 1
1 1 2 1
2 1 3 1
3 1 12 2
4 1 13 2
5 1 14 2
6 2 1 3
7 2 2 3
8 3 5 3
9 3 6 4
10 3 7 4
11 3 8 4
12 3 50 5
I appreciate your suggestions, because I can not find an ingenious way to
do it. Thanks
Is this what you need ?
df.B.diff().ne(1).cumsum()
Out[463]:
0 1
1 1
2 1
3 2
4 2
5 2
6 3
7 3
8 4
9 4
10 4
11 4
12 5
Name: B, dtype: int32