I have the case where I want to sanity check labeled data. I have hundreds of features and want to find points which have the same features but different label. These found cluster of disagreeing labels should then be numbered and put into a new dataframe.
This isn't hard but I am wondering what the most elegant solution for this is.
Here an example:
import pandas as pd
df = pd.DataFrame({
"feature_1" : [0,0,0,4,4,2],
"feature_2" : [0,5,5,1,1,3],
"label" : ["A","A","B","B","D","A"]
})
result_df = pd.DataFrame({
"cluster_index" : [0,0,1,1],
"feature_1" : [0,0,4,4],
"feature_2" : [5,5,1,1],
"label" : ["A","B","B","D"]
})
In order to get the output you want (both de-duplication and cluster_index), you can use a groupby approach:
g = df.groupby(['feature_1', 'feature_2'])['label']
(df.assign(cluster_index=g.ngroup()) # get group name
.loc[g.transform('size').gt(1)] # filter the non-duplicates
# line below only to have a nice cluster_index range (0,1…)
.assign(cluster_index= lambda d: d['cluster_index'].factorize()[0])
)
output:
feature_1 feature_2 label cluster_index
1 0 5 A 0
2 0 5 B 0
3 4 1 B 1
4 4 1 D 1
First get all duplicated values per feature columns and then if necessary remove duplciated by all columns (here in sample data not necessary), last add GroupBy.ngroup for groups indices:
df = df[df.duplicated(['feature_1','feature_2'],keep=False)].drop_duplicates()
df['cluster_index'] = df.groupby(['feature_1', 'feature_2'])['label'].ngroup()
print (df)
feature_1 feature_2 label cluster_index
1 0 5 A 0
2 0 5 B 0
3 4 1 B 1
4 4 1 D 1
I have a dataframe where I need to sort the contents of one column (comma separated) alphabetically:
ID Data
1 Mo,Ab,ZZz
2 Ab,Ma,Bt
3 Xe,Aa
4 Xe,Re,Fi,Ab
Output:
ID Data
1 Ab,Mo,ZZz
2 Ab,Bt,Ma
3 Aa,Xe
4 Ab,Fi,Re,Xe
I have tried:
df.sort_values(by='Data')
But this does not work
You can split, sorting and then join back:
df['Data'] = df['Data'].apply(lambda x: ','.join(sorted(x.split(','))))
Or use list comprehension alternative:
df['Data'] = [','.join(sorted(x.split(','))) for x in df['Data']]
print (df)
ID Data
0 1 Ab,Mo,ZZz
1 2 Ab,Bt,Ma
2 3 Aa,Xe
3 4 Ab,Fi,Re,Xe
IIUC get_dummies
s=df.Data.str.get_dummies(',')
df['n']=s.dot(s.columns+',').str[:-1]
df
Out[216]:
ID Data n
0 1 Mo,Ab,ZZz Ab,Mo,ZZz
1 2 Ab,Ma,Bt Ab,Bt,Ma
2 3 Xe,Aa Aa,Xe
3 4 Xe,Re,Fi,Ab Ab,Fi,Re,Xe
IIUC you can use a list comprehension:
[','.join(sorted(i.split(','))) for i in df['Data']]
#['Ab,Mo,ZZz', 'Ab,Bt,Ma', 'Aa,Xe', 'Ab,Fi,Re,Xe']
using explode and sort_values
df["Sorted_Data"] = (
df["Data"].str.split(",").explode().sort_values().groupby(level=0).agg(','.join)
)
print(df)
ID Data Sorted_Data
0 1 Mo,Ab,ZZz Ab,Mo,ZZz
1 2 Ab,Ma,Bt Ab,Bt,Ma
2 3 Xe,Aa Aa,Xe
3 4 Xe,Re,Fi,Ab Ab,Fi,Re,Xe
Using row iteration:
for index, row in df.iterrows():
row['Data'] = ','.join(sorted(row['Data'].split(',')))
In [29]: df
Out[29]:
Data
0 Ab,Mo,ZZz
1 Ab,Bt,Ma
2 Aa,Xe
3 Ab,Fi,Re,Xe
I have a dataframe
A B C
1 2 3
2 3 4
3 8 7
I want to take only rows where there is a sequence of 3,4 in columns C (in this scenario - first two rows)
What will be the best way to do so?
You can use rolling for general solution working with any pattern:
pat = np.asarray([3,4])
N = len(pat)
mask= (df['C'].rolling(window=N , min_periods=N)
.apply(lambda x: (x==pat).all(), raw=True)
.mask(lambda x: x == 0)
.bfill(limit=N-1)
.fillna(0)
.astype(bool))
df = df[mask]
print (df)
A B C
0 1 2 3
1 2 3 4
Explanation:
use rolling.apply and test pattern
replace 0s to NaNs by mask
use bfill with limit for filling first NANs values by last previous one
fillna NaNs to 0
last cast to bool by astype
Use shift
In [1085]: s = df.eq(3).any(1) & df.shift(-1).eq(4).any(1)
In [1086]: df[s | s.shift()]
Out[1086]:
A B C
0 1 2 3
1 2 3 4
i have a dataframe
id main_value
1 10
2 3
4 1
6 10
i want to change main_value of id = 4,such that it should decrement by 2.
i know a method using .loc
freq = 3
if freq == 3:
df.loc[df.id==4, ['main_value']] = df.main_value.loc[df.id==4] - 2
But this seems very lengthy, is there a better way to do this?
I think you can use:
df.loc[df.id==4, 'main_value'] -= 2
print (df)
id main_value
0 1 10
1 2 3
2 4 -1
3 6 10
I have a super strange problem which I spent the last hour trying to solve, but with no success. It is even more strange since I can't replicate it on a small scale.
I have a large DataFrame (150,000 entries). I took out a subset of it and did some manipulation. the subset was saved as a different variable, x.
x is smaller than the df, but its index is in the same range as the df. I'm now trying to assign x back to the DataFrame replacing values in the same column:
rep_Callers['true_vpID'] = x.true_vpID
This inserts all the different values in x to the right place in df, but instead of keeping the df.true_vpID values that are not in x, it is filling them with NaNs. So I tried a different approach:
df.ix[x.index,'true_vpID'] = x.true_vpID
But instead of filling x values in the right place in df, the df.true_vpID gets filled with the first value of x and only it! I changed the first value of x several times to make sure this is indeed what is happening, and it is. I tried to replicate it on a small scale but it didn't work:
df = DataFrame({'a':ones(5),'b':range(5)})
a b
0 1 0
1 1 1
2 1 2
3 1 3
4 1 4
z =Series([random() for i in range(5)],index = range(5))
0 0.812561
1 0.862109
2 0.031268
3 0.575634
4 0.760752
df.ix[z.index[[1,3]],'b'] = z[[1,3]]
a b
0 1 0.000000
1 1 0.812561
2 1 2.000000
3 1 0.575634
4 1 4.000000
5 1 5.000000
I really tried it all, need some new suggestions...
Try using df.update(updated_df_or_series)
Also using a simple example, you can modify a DataFrame by doing an index query and modifying the resulting object.
df_1
a b
0 1 0
1 1 1
2 1 2
3 1 3
4 1 4
df_2 = df_1.ix[3:5]
df_2.b = df_2.b + 2
df_2
a b
3 1 5
4 1 6
df_1
a b
0 1 0
1 1 1
2 1 2
3 1 5
4 1 6