I have a Data frame as below :
Col1 Col2 Col3 Col4
1 111 a Test
2 111 b Test
3 111 c Test
4 222 d Prod
5 333 e Prod
6 333 f Prod
7 444 g Test
8 555 h Prod
9 555 i Prod
Expected output :
Column 1 Column 2 Relationship Count
Col2 Col3 One-to-One 2
Col2 Col3 One-to-Many 3
Explanation :
I need to identify the relationship between Col2 & Col3 and also the value counts.
For Eg. 111(col2) is repeated 3 times and has 3 different respective values a,b,c in Col3.
This means col2 and col3 has one-to-Many relationship - count_1 : 1
222(col2) is not repeated and has only one respective value d in col3.
This means col2 and col3 has one-to-one relationshipt - count_2 : 1
333(col2) is repeated twice and has 2 different respective values e,f in col3.
This means col2 and col3 has one-to-Many relationship - count_1 : 1+1 ( increment this count for every one-to-many relationship)
Similarly for other column values increment the respective counter and display the final results as the expected dataframe.
If you only need to check the relationship between col2 and col3, you can do:
(
df.groupby(by='Col2').Col3
.apply(lambda x: 'One-to-One' if len(x)==1 else 'One-to-Many')
.to_frame('Relationship')
.groupby('Relationship').Relationship
.count().to_frame('Count').reset_index()
.assign(**{'Column 1':'Col2', 'Column 2':'Col3'})
.reindex(columns=['Column 1', 'Column 2', 'Relationship', 'Count'])
)
Output:
Column 1 Column 2 Relationship Count
0 Col2 Col3 One-to-Many 3
1 Col2 Col3 One-to-One 2
Related
There are paired columns that I am comparing(col1 and col2, col3 and col4) with either blank or '0' or '1'. I basically want to know how many are intersect
id col1 col2 col3 col4
id1 0 1
id2 1 1 0
id3 0 1 1
id4
id5 0
for this table I want to count of how many ids are 0 or 1(between col1 and col2). If I use countA(b2:c4) I get 4 but I need to get 3 as only 3 ids are affected for each pair
. Is therea formula that would actually give 3 for col1 and col2 and 3 for col3 and col4.
SUMPRODUCT(--(B$2:B$7+C$2:C$7=0))
fails here and provides 3 instead of 5
I have a pandas dataframe as below. I want to group by based on all the three columns and retain the group with the max of Col1.
import pandas as pd
df = pd.DataFrame({'col1':['A', 'A', 'A', 'A', 'B', 'B'], 'col2':['1', '1', '1', '1', '2', '3'], 'col3':['5', '5', '2', '2', '2', '3']})
df
col1 col2 col3
0 A 1 5
1 A 1 5
2 A 1 2
3 A 1 2
4 B 2 2
5 B 3 3
My expected output
col1 col2 col3
0 A 1 5
1 A 1 5
4 B 2 2
5 B 3 3
I tried below code, but it return me the last row of each group, instead I want to sort by col3 and keep the group with max col3
df.drop_duplicates(keep='last', subset=['col1','col2','col3'])
col1 col2 col3
1 A 1 5
3 A 1 2
4 B 2 2
5 B 3 3
For Example: Here I want to drop 1st group because 2 < 5, so I want to keep the group with col3 as 5
df.sort_values(by=['col1', 'col2', 'col3'], ascending=False)
a_group = df.groupby(['col1', 'col2', 'col3'])
for name, group in a_group:
group = group.reset_index(drop=True)
print(group)
col1 col2 col3
0 A 1 2
1 A 1 2
col1 col2 col3
0 A 1 5
1 A 1 5
col1 col2 col3
0 B 2 2
col1 col2 col3
0 B 3 3
You cant group on all columns since the col you wish to retain max for has different values. Instead dont include that column in the group and consider others:
col_to_max = 'col3'
i = df.columns ^ [col_to_max]
out = df[df[col_to_max] == df.groupby(list(i))[col_to_max].transform('max')]
print(out)
col1 col2 col3
0 A 1 5
1 A 1 5
4 B 2 2
5 B 3 3
So we can do
out = df[df.col3==df.groupby(['col1','col2'])['col3'].transform('max')]
col1 col2 col3
0 A 1 5
1 A 1 5
4 B 2 2
5 B 3 3
I believe you can use groupby with nlargest(2). Also make sure that your 'col3' is a numerical one.
>>> df['col3'] = df['col3'].astype(int)
>>> df.groupby(['col1','col2'])['col3'].nlargest(2).reset_index().drop('level_2',axis=1)
col1 col2 col3
0 A 1 5
1 A 1 5
2 B 2 2
3 B 3 3
You can get index which doesn't has col3 max value and duplicated index and drop the intersection
ind = df.assign(max = df.groupby("col1")["col3"].transform("max")).query("max != col3").index
ind2 = df[df.duplicated(keep=False)].index
df.drop(set(ind).intersection(ind2))
col1 col2 col3
0 A 1 5
1 A 1 5
4 B 2 2
5 B 3 3
I would like to know how to pass from a multiindex dataframe like this:
A B
col1 col2 col1 col2
1 2 12 21
3 1 2 0
To two separated dfs. df_A:
col1 col2
1 2
3 1
df_B:
col1 col2
12 21
2 0
Thank you for the help
I think here is better use DataFrame.xs for selecting by first level:
print (df.xs('A', axis=1, level=0))
col1 col2
0 1 2
1 3 1
What need is not recommended, but possible create DataFrames by groups:
for i, g in df.groupby(level=0, axis=1):
globals()['df_' + str(i)] = g.droplevel(level=0, axis=1)
print (df_A)
col1 col2
0 1 2
1 3 1
Better is create dictionary of DataFrames:
d = {i:g.droplevel(level=0, axis=1)for i, g in df.groupby(level=0, axis=1)}
print (d['A'])
col1 col2
0 1 2
1 3 1
I am trying to groupby columns value in pandas but I'm not getting.
Example:
Col1 Col2 Col3
A 1 2
B 5 6
A 3 4
C 7 8
A 11 12
B 9 10
-----
result needed grouping by Col1
Col1 Col2 Col3
A 1,3,11 2,4,12
B 5,9 6,10
c 7 8
but I getting this ouput
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000025BEB4D6E50>
I am getting using excel power query with function group by and count all rows, but I can´t get the same with python and pandas. Any help?
Try this
(
df
.groupby('Col1')
.agg(lambda x: ','.join(x.astype(str)))
.reset_index()
)
it outputs
Col1 Col2 Col3
0 A 1,3,11 2,4,12
1 B 5,9 6,10
2 C 7 8
Very good I created solution between 0 and 0:
df[df['A'] != 0].groupby((df['A'] == 0).cumsum()).sub()
It will group column between 0 and 0 and sum it
I have an Excel Sheet Dataframe with no fixed number of rows and columns.
eg.
Col1 Col2 Col3
A 1 -
A - 2
B 3 -
B - 4
C 5 -
I would like to Group Col1 which has the same content. Like the following.
Col1 Col2 Col3
A 1 2
B 3 4
C 5 -
I am using pandas GroupBy, but not getting what I wanted.
Try using groupby:
print(df.replace('-', pd.np.nan).groupby('Col1', as_index=False).first().fillna('-'))
Output:
Col1 Col2 Col3
0 A 1 2
1 B 3 4
2 C 5 -