I have dataframe df with following data.
A B C D
1 1 3 1
1 2 9 8
1 3 3 9
2 1 2 9
2 2 1 4
2 3 9 5
2 4 6 4
3 1 4 1
3 2 0 4
4 1 2 6
5 1 2 4
5 2 8 3
grp = df.groupby('A')
Next I want to make all groups of dataframe df grouped on columns A to have same number of rows. Either Truncate extra rows or pad 0 rows. For above data, I want to make all groups to have 3 rows. I required the following results.
A B C D
1 1 3 1
1 2 9 8
1 3 3 9
2 1 2 9
2 2 1 4
2 3 9 5
3 1 4 1
3 2 0 4
3 0 0 0
4 1 2 6
4 0 0 0
4 0 0 0
5 1 2 4
5 2 8 3
5 0 0 0
Similarly, I may want to groupby on multiple columns, like
grp = df.groupby(['A','B'])
Use GroupBy.cumcount for counter column with DataFrame.reindex by MultiIndex.from_product:
df['g'] = df.groupby('A').cumcount()
mux = pd.MultiIndex.from_product([df['A'].unique(), range(3)], names=('A','g'))
df = (df.set_index(['A','g'])
.reindex(mux, fill_value=0)
.reset_index(level=1, drop=True)
.reset_index())
print (df)
A B C D
0 1 1 3 1
1 1 2 9 8
2 1 3 3 9
3 2 1 2 9
4 2 2 1 4
5 2 3 9 5
6 3 1 4 1
7 3 2 0 4
8 3 0 0 0
9 4 1 2 6
10 4 0 0 0
11 4 0 0 0
12 5 1 2 4
13 5 2 8 3
14 5 0 0 0
Another solution with DataFrame.merge with left join with helper DataFrame:
from itertools import product
df['g'] = df.groupby('A').cumcount()
df1 = pd.DataFrame(list(product(df['A'].unique(), range(3))), columns=['A','g'])
df = df1.merge(df, how='left').fillna(0).astype(int).drop('g', axis=1)
print (df)
A B C D
0 1 1 3 1
1 1 2 9 8
2 1 3 3 9
3 2 1 2 9
4 2 2 1 4
5 2 3 9 5
6 3 1 4 1
7 3 2 0 4
8 3 0 0 0
9 4 1 2 6
10 4 0 0 0
11 4 0 0 0
12 5 1 2 4
13 5 2 8 3
14 5 0 0 0
EDIT:
df['g'] = df.groupby(['A','B']).cumcount()
mux = pd.MultiIndex.from_product([df['A'].unique(),
df['B'].unique(),
range(3)], names=('A','B','g'))
df = (df.set_index(['A','B','g'])
.reindex(mux, fill_value=0)
.reset_index(level=2, drop=True)
.reset_index())
print (df.head(10))
A B C D
0 1 1 3 1
1 1 1 0 0
2 1 1 0 0
3 1 2 9 8
4 1 2 0 0
5 1 2 0 0
6 1 3 3 9
7 1 3 0 0
8 1 3 0 0
9 1 4 0 0
from itertools import product
df['g'] = df.groupby(['A','B']).cumcount()
df1 = pd.DataFrame(list(product(df['A'].unique(),
df['B'].unique(),
range(3))), columns=['A','B','g'])
df = df1.merge(df, how='left').fillna(0).astype(int).drop('g', axis=1)
Related
Assume DF 1:
A B C
0 1 1 1
1 1 1 2
2 2 1 1
3 1 9 0
4 9 9 9
And DF 2
A B C
0 6 1 1
1 1 1 2
2 2 1 1
3 1 9 0
4 1 9 6
I would like to add a column to DF 1 with a count of duplicates in DF 2 based on a subset of columns:
For example
Duplicate on
1
2
Result:
A B C Dupe
0 1 1 1 1
1 1 1 2 1
2 2 1 1 1
3 1 9 0 2
4 9 9 9 0
Sound like you should groupby by df2 then merge
df=df1.merge(df2.groupby(['A','B']).size().to_frame('DUP').reset_index(),how='left').fillna(0)
A B C DUP
0 1 1 1 1.0
1 1 1 2 1.0
2 2 1 1 1.0
3 1 9 0 2.0
4 9 9 9 0.0
I've got a df
df1
a b
4 0 1
5 0 1
6 0 2
2 0 3
3 1 2
15 1 3
12 1 3
13 1 1
15 3 1
14 3 1
8 3 3
9 3 2
10 3 1
the df should be grouped by a and b and I need a column c that goes up from 1 to amount of groups within subgroups of a
df1
a b c
4 0 1 1
5 0 1 1
6 0 2 2
2 0 3 3
3 1 2 1
15 1 3 2
12 1 3 2
13 1 1 3
15 3 1 1
14 3 1 1
8 3 3 2
9 3 2 3
10 3 1 4
How can I do that?
We can do groupby + transform factorize
df['C']=df.groupby('a').b.transform(lambda x : x.factorize()[0]+1)
4 1
5 1
6 2
2 3
3 1
15 2
12 2
13 3
15 1
14 1
8 1
9 1
10 2
Name: b, dtype: int64
Just so we can see the loop version
from itertools import count
from collections import defaultdict
x = defaultdict(count)
y = {}
c = []
for a, b in zip(df.a, df.b):
if (a, b) not in y:
y[(a, b)] = next(x[a]) + 1
c.append(y[(a, b)])
df.assign(C=c)
a b C
4 0 1 1
5 0 1 1
6 0 2 2
2 0 3 3
3 1 2 1
15 1 3 2
12 1 3 2
13 1 1 3
15 3 1 1
14 3 1 1
8 3 3 2
9 3 2 3
10 3 1 1
One option is groupby a and then iterate through each group and groupby b. Then use can use ngroup
df['c'] = np.hstack([g.groupby('b').ngroup().to_numpy() for _,g in df.groupby('a')])
a b c
4 0 1 0
5 0 1 0
6 0 2 1
2 0 3 2
3 1 2 1
15 1 3 2
12 1 3 2
13 1 1 0
15 3 1 0
14 3 1 0
8 3 1 0
9 3 1 0
10 3 2 1
you can use groupby.rank if you don't care about the order in the data.
df['c'] = df.groupby('a')['b'].rank('dense').astype(int)
Currently, I have a dataframe like this:
0 0 0 3 0 0
0 7 8 9 1 0
0 4 5 2 4 0
My code to stack it:
dt = dataset.iloc[:,0:7].stack().sort_index(level=1).reset_index(level=0, drop=True).to_frame()
dt['variable'] = pandas.Categorical(dt.index).codes+1
dt.rename(columns={0:index_column_name}, inplace=True)
dt.set_index(index_column_name, inplace=True)
dt['variable'] = numpy.sort(dt['variable'])
However, it drops the first row when I'm stacking it, and I want to keep the headers / first row, how would I achieve this?
In essence, I'm losing the data from the first row (a.k.a column headers) and I want to keep it.
Desired Output:
value,variable
0 1
0 1
0 1
0 2
7 2
4 2
0 3
8 3
5 3
3 4
9 4
2 4
0 5
1 5
4 5
0 6
0 6
0 6
Current output:
value,variable
0 1
0 1
7 2
4 2
8 3
5 3
9 4
2 4
1 5
4 5
0 6
0 6
Why not use df.melt as #WeNYoBen mentioned?
print(df)
1 2 3 4 5 6
0 0 0 0 3 0 0
1 0 7 8 9 1 0
2 0 4 5 2 4 0
print(df.melt())
variable value
0 1 0
1 1 0
2 1 0
3 2 0
4 2 7
5 2 4
6 3 0
7 3 8
8 3 5
9 4 3
10 4 9
11 4 2
12 5 0
13 5 1
14 5 4
15 6 0
16 6 0
17 6 0
I've succeed on splitting a DataFrame into several smaller DataFrames. I'm now working on giving these DataFrames sequential names, and can be called independently.
shuffled = df.sample(frac=1)
result = np.array_split(shuffled, 3)
for part in result:
print(part, '\n')
movie_id 1 2 5 borda rank IRAM
2 3 4 0 0 4 3 2
1 2 3 0 3 6 2 1
movie_id 1 2 5 borda rank IRAM
4 5 3 0 0 3 4 3
0 1 5 4 4 13 1 4
movie_id 1 2 5 borda rank IRAM
3 4 3 0 0 3 4 3
I want to give names in sequential order to these separated DataFrames with loop(or any helpful methods).
For instance :
df_1
movie_id 1 2 5 borda rank IRAM
2 3 4 0 0 4 3 2
1 2 3 0 3 6 2 1
df_2
movie_id 1 2 5 borda rank IRAM
4 5 3 0 0 3 4 3
0 1 5 4 4 13 1 4
df_3
movie_id 1 2 5 borda rank IRAM
3 4 3 0 0 3 4 3
I've been searching solutions for a while, but I can't find an ideally answer to my problem.
This can be done by taking a dictionary and adding all dataframes into it:
df = pd.DataFrame({'Col1': np.random.randint(10, size=10)})
shuffled = df.sample(frac=1)
result = np.array_split(shuffled, 3)
d = {}
for i, part in enumerate(result):
d['df_'+str(i)] = part # If want to start the number for df from 1 then use str(i+1)
print(d['df_0'])
Col1
7 7
6 0
4 5
2 3
print(d['df_1'])
Col1
0 0
8 1
1 5
print(d['df_2'])
Col1
5 2
3 2
9 4
df_dict = {}
for index, splited in enumerate(result):
df_name = "df_{}".format(index)
# if you want to set name of the dataframe
splited.name = df_name
# if you want to set the variable name to dataframe
df_dict[df_name] = splited
print(df_dict)
{'df_0': movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
9 10 3 2 0 0 0 4 0 0 0 0 0 9
7 8 1 0 0 0 4 5 0 0 0 4 0 14
6 7 4 0 0 0 2 5 3 4 4 0 0 22
0 1 5 4 0 4 4 0 0 0 4 0 0 21,
'df_1': movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
8 9 5 0 0 0 4 5 0 0 4 5 0 23
3 4 3 0 0 0 0 5 0 0 4 0 5 17
5 6 5 0 0 0 0 0 0 5 0 0 0 10,
'df_2': movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
4 5 3 0 0 0 0 0 0 0 0 0 0 3
2 3 4 0 0 0 0 0 0 0 0 0 0 4
1 2 3 0 0 3 0 0 0 0 0 0 0 6}
Then you can call any splited_df by df_dict[df_name].
You can use a dictionary, like this:
d = {"df_"+str(k):v for (k,v) in [(i,result[i]) for i in range(len(result))]}
I have a dataframe now：
class1 class2 value value2
0 1 0 1 4
1 2 1 2 3
2 2 0 3 5
3 3 1 4 6
I want to repeat rows and insert an increment column in the same amount according to the difference between value and value2. I want to get the dataframe should like this:
class1 class2 value value2 value3
0 1 0 1 4 1
1 1 0 1 4 2
2 1 0 1 4 3
3 1 0 1 4 4
4 2 1 2 3 2
5 2 1 2 3 3
6 2 0 3 5 3
7 2 0 3 5 4
8 2 0 3 5 5
9 3 1 4 6 4
10 3 1 4 6 5
11 3 1 4 6 6
I tried it like：
def func(x):
copy = x.copy()
num = x.value2+1-x.value
return pd.concat([copy]*num.values[0])
df= df.groupby(['class1','class2']).apply(lambda x:func(x))
But there will be a oredr problem that leads me to not know how to add column value3. And I'd like to have an elegant way of doing it.
Can anyone help me? Thanks in advance
Compute the difference and call Index.repeat:
idx = df.index.repeat(df.value2 - df.value + 1)
Now, either use reindex:
df = df.reindex(idx).reset_index(drop=True)
Or loc:
df = df.loc[idx].reset_index(drop=True)
And you get
df
class1 class2 value value2
0 1 0 1 4
1 1 0 1 4
2 1 0 1 4
3 1 0 1 4
4 2 1 2 3
5 2 1 2 3
6 2 0 3 5
7 2 0 3 5
8 2 0 3 5
9 3 1 4 6
10 3 1 4 6
11 3 1 4 6
For the second part of your question, you'll need groupby.cumcount:
s = idx.to_series()
df['value3'] = df['value'] + s.groupby(idx).cumcount().values
df
class1 class2 value value2 value3
0 1 0 1 4 1
1 1 0 1 4 2
2 1 0 1 4 3
3 1 0 1 4 4
4 2 1 2 3 2
5 2 1 2 3 3
6 2 0 3 5 3
7 2 0 3 5 4
8 2 0 3 5 5
9 3 1 4 6 4
10 3 1 4 6 5
11 3 1 4 6 6
Here's a sequence of things that would get you the desired output:
df.join(df
.apply(lambda x: pd.Series(range(x.value, x.value2+1)), axis=1)
.stack().astype(int)
.reset_index(level=1, drop=1)
.to_frame('value3')).reset_index(drop=1)
Out[]:
class1 class2 value value2 value3
0 1 0 1 4 1
1 1 0 1 4 2
2 1 0 1 4 3
3 1 0 1 4 4
4 2 1 2 3 2
5 2 1 2 3 3
6 2 0 3 5 3
7 2 0 3 5 4
8 2 0 3 5 5
9 3 1 4 6 4
10 3 1 4 6 5
11 3 1 4 6 6