which rows are duplicates to each other - pandas

I have got a database with a lot of columns. Some of the rows are duplicates (on a certain subset).
Now I want to find out which row duplicates which row and put them together.
For instance, let's suppose that the data frame is
id A B C
0 0 1 2 0
1 1 2 3 4
2 2 1 4 8
3 3 1 2 3
4 4 2 3 5
5 5 5 6 2
and subset is
['A','B']
I expect something like this:
id A B C
0 0 1 2 0
1 3 1 2 3
2 1 2 3 4
3 4 2 3 5
4 2 1 4 8
5 5 5 6 2
Is there any function that can help me do this?
Thanks :)


Use DataFrame.duplicated with keep=False for mask with all dupes, then flter by boolean indexing, sorting by DataFrame.sort_values and join together by concat:
L = ['A','B']
m = df.duplicated(L, keep=False)
df = pd.concat([df[m].sort_values(L), df[~m]], ignore_index=True)
print (df)
id A B C
0 0 1 2 0
1 3 1 2 3
2 1 2 3 4
3 4 2 3 5
4 2 1 4 8
5 5 5 6 2

Related

Pandas concat function with count assigned for each iteration

At the replication of a dataframe using concat with index (see example here), is there a way I can assign a count variable for each iteration in column c (where column c is the count variable)?
Orig df:
a
b
0
1
2
1
2
3
df replicated with pd.concat[df]*5 and with an additional Column c:
a
b
c
0
1
2
1
1
2
3
1
0
1
2
2
1
2
3
2
0
1
2
3
1
2
3
3
0
1
2
4
1
2
3
4
0
1
2
5
1
2
3
5
This is a multi-row dataframe where the count variable would have to be applied to multiple rows.
Thanks for your thoughts!

You could use np.arange and np.repeat:
N = 5
new_df = pd.concat([df] * N)
new_df['c'] = np.repeat(np.arange(N), df.shape[0]) + 1
Output:
>>> new_df
a b c
0 1 2 1
1 2 3 1
0 1 2 2
1 2 3 2
0 1 2 3
1 2 3 3
0 1 2 4
1 2 3 4
0 1 2 5
1 2 3 5

If a column value does not have a certain number of occurances in a dataframe, how to duplicate rows at random until that count is met?

Say that this is what my dataframe looks like
A B
0 1 5
1 4 2
2 3 5
3 3 3
4 3 2
5 2 0
6 4 5
7 2 3
8 4 1
9 5 1
I want every unique value in Column B to occur at least 3 times. So none of the rows with a B value of 5 are duplicated. The row with a column B value of 0 are duplicated twice. And the rest have one of their two rows duplicated at random.
Here is an example desired output
A B
0 1 5
1 4 2
2 3 5
3 3 3
4 3 2
5 2 0
6 4 5
7 2 3
8 4 1
9 5 1
10 4 2
11 2 3
12 2 0
13 2 0
14 4 1
Edit:
The row chosen to be duplicated should be selected at random

To random pick rows, I would use groupby apply with sample on each group. x of lambda is each group of B, so I use reapeat - x.shape[0] to find number of rows need to create. There may be some cases group B already has more rows than 3, so I use np.clip to force negative values to 0. Sample on 0 row is the same as ignore it. Finally, reset_index and append back to df
repeats = 3
df1 = (df.groupby('B').apply(lambda x: x.sample(n=np.clip(repeats-x.shape[0], 0, np.inf)
.astype(int), replace=True))
.reset_index(drop=True))
df_final = df.append(df1).reset_index(drop=True)
Out[43]:
A B
0 1 5
1 4 2
2 3 5
3 3 3
4 3 2
5 2 0
6 4 5
7 2 3
8 4 1
9 5 1
10 2 0
11 2 0
12 5 1
13 4 2
14 2 3

Comparing two dataframe and output the index of the duplicated row once

I need help with comparing two dataframes. For example:
The first dataframe is
df_1 =
0 1 2 3 4 5
0 1 1 1 1 1 1
1 2 2 2 2 2 2
2 3 3 3 3 3 3
3 4 4 4 4 4 4
4 2 2 2 2 2 2
5 5 5 5 5 5 5
6 1 1 1 1 1 1
7 6 6 6 6 6 6
The second dataframe is
df_2 =
0 1 2 3 4 5
0 1 1 1 1 1 1
1 2 2 2 2 2 2
2 3 3 3 3 3 3
3 4 4 4 4 4 4
4 5 5 5 5 5 5
5 6 6 6 6 6 6
May I know if there is a way (without using for loop) to find the index of the rows of df_1 that have the same row values of df_2. In the example above, my expected output is below
index =
0
1
2
3
5
7
The size of the column of the "index" variable above should have the same column size of df_2.
If the same row of df_2 repeated in df_1 more than once, I only need the index of the first appearance, thats why I don't need the index 4 and 6.
Please help. Thank you so much!
Tommy

Use DataFrame.merge with DataFrame.drop_duplicates and DataFrame.reset_index for convert index to column for avoid lost index values, last select column called index:
s = df_2.merge(df_1.drop_duplicates().reset_index())['index']
print (s)
0 0
1 1
2 2
3 3
4 5
5 7
Name: index, dtype: int64
Detail:
print (df_2.merge(df_1.drop_duplicates().reset_index()))
0 1 2 3 4 5 index
0 1 1 1 1 1 1 0
1 2 2 2 2 2 2 1
2 3 3 3 3 3 3 2
3 4 4 4 4 4 4 3
4 5 5 5 5 5 5 5
5 6 6 6 6 6 6 7

Check the solution
df1=pd.DataFrame({'0':[1,2,3,4,2,5,1,6],
'1':[1,2,3,4,2,5,1,6],
'2':[1,2,3,4,2,5,1,6],
'3':[1,2,3,4,2,5,1,6],
'4':[1,2,3,4,2,5,1,6],
'5':[1,2,3,4,2,5,1,6]})
df1=pd.DataFrame({'0':[1,2,3,4,5,6],
'1':[1,2,3,4,5,66],
'2':[1,2,3,4,5,6],
'3':[1,2,3,4,5,66],
'4':[1,2,3,4,5,6],
'5':[1,2,3,4,5,6]})
df1[df1.isin(df2)].index.values.tolist()
### Output
[0, 1, 2, 3, 4, 5, 6, 7]

Pandas count values inside dataframe

I have a dataframe that looks like this:
A B C
1 1 8 3
2 5 4 3
3 5 8 1
and I want to count the values so to make df like this:
total
1 2
3 2
4 1
5 2
8 2
is it possible with pandas?

With np.unique -
In [332]: df
Out[332]:
A B C
1 1 8 3
2 5 4 3
3 5 8 1
In [333]: ids, c = np.unique(df.values.ravel(), return_counts=1)
In [334]: pd.DataFrame({'total':c}, index=ids)
Out[334]:
total
1 2
3 2
4 1
5 2
8 2
With pandas-series -
In [357]: pd.Series(np.ravel(df)).value_counts().sort_index()
Out[357]:
1 2
3 2
4 1
5 2
8 2
dtype: int64

You can also use stack() and groupby()
df = pd.DataFrame({'A':[1,8,3],'B':[5,4,3],'C':[5,8,1]})
print(df)
A B C
0 1 5 5
1 8 4 8
2 3 3 1
df1 = df.stack().reset_index(1)
df1.groupby(0).count()
level_1
0
1 2
3 2
4 1
5 2
8 2

Other alternative may be to use stack, followed by value_counts then, result changed to frame and finally sorting the index:
count_df = df.stack().value_counts().to_frame('total').sort_index()
count_df
Result:
total
1 2
3 2
4 1
5 2
8 2

using np.unique(, return_counts=True) and np.column_stack():
pd.DataFrame(np.column_stack(np.unique(df, return_counts=True)))
returns:
0 1
0 1 2
1 3 2
2 4 1
3 5 2
4 8 2

compare two column of two dataframe pandas

I have 2 data frames like :
df_out:
a b c d
1 1 2 1
2 1 2 3
3 1 3 5
df_fin:
a e f g
1 0 2 1
2 5 2 3
3 1 3 5
5 2 4 6
7 3 2 5
I want to get result as :
a b c d a e f g
1 1 2 1 1 0 2 1
2 1 2 3 2 5 2 3
3 1 3 5 3 1 3 5
in the other word I have two diffrent data frames that are common in one column(a), I want two compare this two columns(df_fin.a and df_out.a) and select the rows from df_fin that have the same value in column a and create new dataframe that has selected rows from df_fin and added columns from df_out ?

I think you need merge with left join:
df = pd.merge(df_out, df_fin, on='a', how='left')
print (df)
a b c d e f g
0 1 1 2 1 0 2 1
1 2 1 2 3 5 2 3
2 3 1 3 5 1 3 5
EDIT:
df1 = df_fin[df_fin['a'].isin(df_out['a'])]
df2 = df_out.join(df1.set_index('a'), on='a')
print (df2)
a b c d e f g
0 1 1 2 1 0 2 1
1 2 1 2 3 5 2 3
2 3 1 3 5 1 3 5