I am using the label encoder to convert categorical data into numeric values.
How does LabelEncoder handle missing values?
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import numpy as np
a = pd.DataFrame(['A','B','C',np.nan,'D','A'])
le = LabelEncoder()
le.fit_transform(a)
Output:
array([1, 2, 3, 0, 4, 1])
For the above example, label encoder changed NaN values to a category. How would I know which category represents missing values?
Don't use LabelEncoder with missing values. I don't know which version of scikit-learn you're using, but in 0.17.1 your code raises TypeError: unorderable types: str() > float().
As you can see in the source it uses numpy.unique against the data to encode, which raises TypeError if missing values are found. If you want to encode missing values, first change its type to a string:
a[pd.isnull(a)] = 'NaN'
you can also use a mask to replace form the original data frame after labelling
df = pd.DataFrame({'A': ['x', np.NaN, 'z'], 'B': [1, 6, 9], 'C': [2, 1, np.NaN]})
A B C
0 x 1 2.0
1 NaN 6 1.0
2 z 9 NaN
original = df
mask = df_1.isnull()
A B C
0 False False False
1 True False False
2 False False True
df = df.astype(str).apply(LabelEncoder().fit_transform)
df.where(~mask, original)
A B C
0 1.0 0 1.0
1 NaN 1 0.0
2 2.0 2 NaN
Hello a little computational hack I did for my own work:
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import numpy as np
a = pd.DataFrame(['A','B','C',np.nan,'D','A'])
le = LabelEncoder()
### fit with the desired col, col in position 0 for this example
fit_by = pd.Series([i for i in a.iloc[:,0].unique() if type(i) == str])
le.fit(fit_by)
### Set transformed col leaving np.NaN as they are
a["transformed"] = fit_by.apply(lambda x: le.transform([x])[0] if type(x) == str else x)
This is my solution, because I was not pleased with the solutions posted here. I needed a LabelEncoder that keeps my missing values as NaN to use an Imputer afterwards. So I have written my own LabelEncoder class. It works with DataFrames.
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin
from sklearn.preprocessing import LabelEncoder
class LabelEncoderByCol(BaseEstimator, TransformerMixin):
def __init__(self,col):
#List of column names in the DataFrame that should be encoded
self.col = col
#Dictionary storing a LabelEncoder for each column
self.le_dic = {}
for el in self.col:
self.le_dic[el] = LabelEncoder()
def fit(self,x,y=None):
#Fill missing values with the string 'NaN'
x[self.col] = x[self.col].fillna('NaN')
for el in self.col:
#Only use the values that are not 'NaN' to fit the Encoder
a = x[el][x[el]!='NaN']
self.le_dic[el].fit(a)
return self
def transform(self,x,y=None):
#Fill missing values with the string 'NaN'
x[self.col] = x[self.col].fillna('NaN')
for el in self.col:
#Only use the values that are not 'NaN' to fit the Encoder
a = x[el][x[el]!='NaN']
#Store an ndarray of the current column
b = x[el].to_numpy()
#Replace the elements in the ndarray that are not 'NaN'
#using the transformer
b[b!='NaN'] = self.le_dic[el].transform(a)
#Overwrite the column in the DataFrame
x[el]=b
#return the transformed DataFrame
return x
You can enter a DataFrame, not only a 1-dim Series. with col you can chose the columns that should be encoded.
I would like to here some feedback.
I want to share with you my solution.
I created a module which take mix dataset and convert it from categorical to numerical
and inverse.
This Module also available in my Github well organized with example.
Please upvoted if you like my solution.
Tks,
Idan
class label_encoder_contain_missing_values :
def __init__ (self) :
pass
def categorical_to_numeric (self,dataset):
import numpy as np
import pandas as pd
self.dataset = dataset
self.summary = None
self.table_encoder= {}
for index in self.dataset.columns :
if self.dataset[index].dtypes == 'object' :
column_data_frame = pd.Series(self.dataset[index],name='column').to_frame()
unique_values = pd.Series(self.dataset[index].unique())
i = 0
label_encoder = pd.DataFrame({'value_name':[],'Encode':[]})
while i <= len(unique_values)-1:
if unique_values.isnull()[i] == True :
label_encoder = label_encoder.append({'value_name': unique_values[i],'Encode':np.nan}, ignore_index=True) #np.nan = -1
else:
label_encoder = label_encoder.append({'value_name': unique_values[i],'Encode':i}, ignore_index=True)
i+=1
output = pd.merge(left=column_data_frame,right = label_encoder, how='left',left_on='column',right_on='value_name')
self.summary = output[['column','Encode']].drop_duplicates().reset_index(drop=True)
self.dataset[index] = output.Encode
self.table_encoder.update({index:self.summary})
else :
pass
# ---- Show Encode Table ----- #
print('''\nLabel Encoding completed in Successfully.\n
Next steps: \n
1. To view table_encoder, Execute the follow: \n
for index in table_encoder :
print(f'\\n{index} \\n',table_encoder[index])
2. For inverse, execute the follow : \n
df = label_encoder_contain_missing_values().
inverse_numeric_to_categorical(table_encoder, df) ''')
return self.table_encoder ,self.dataset
def inverse_numeric_to_categorical (self,table_encoder, df):
dataset = df.copy()
for column in table_encoder.keys():
df_column = df[column].to_frame()
output = pd.merge(left=df_column,right = table_encoder[column], how='left',left_on= column,right_on='Encode')#.rename(columns={'column_x' :'encode','column_y':'category'})
df[column]= output.column
print('\nInverse Label Encoding, from categorical to numerical completed in Successfully.\n')
return df
**execute command from categorical to numerical** <br>
table_encoder, df = label_encoder_contain_missing_values().categorical_to_numeric(df)
**execute command from numerical to categorical** <br>
df = label_encoder_contain_missing_values().inverse_numeric_to_categorical(table_encoder, df)
An easy way is this
It is an example of Titanic
LABEL_COL = ["Sex", "Embarked"]
def label(df):
_df = df.copy()
le = LabelEncoder()
for col in LABEL_COL:
# Not NaN index
idx = ~_df[col].isna()
_df.loc[idx, col] \
= le.fit(_df.loc[idx, col]).transform(_df.loc[idx, col])
return _df
The most voted answer by #Kerem has typos, therefore I am posting the corrected and improved answer here:
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import numpy as np
a = pd.DataFrame(['A','B','C',np.nan,'D','A'])
for j in a.columns.values:
le = LabelEncoder()
### fit with the desired col, col in position 0 for this ###example
fit_by = pd.Series([i for i in a[j].unique() if type(i) == str])
le.fit(fit_by)
### Set transformed col leaving np.NaN as they are
a["transformed"] = a[j].apply(lambda x: le.transform([x])[0] if type(x) == str else x)
You can handle missing values by replacing it with string 'NaN'. The category can be obtained by le.transfrom().
le.fit_transform(a.fillna('NaN'))
category = le.transform(['NaN'])
Another solution is for label encoder to ignore missing values.
a = le.fit_transform(a.astype(str))
You can fill the na's by some value and later change the dataframe column type to string to make things work.
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import numpy as np
a = pd.DataFrame(['A','B','C',np.nan,'D','A'])
a.fillna(99)
le = LabelEncoder()
le.fit_transform(a.astype(str))
Following encoder addresses None values in each category.
class MultiColumnLabelEncoder:
def __init__(self):
self.columns = None
self.led = defaultdict(preprocessing.LabelEncoder)
def fit(self, X):
self.columns = X.columns
for col in self.columns:
cat = X[col].unique()
cat = [x if x is not None else "None" for x in cat]
self.led[col].fit(cat)
return self
def fit_transform(self, X):
if self.columns is None:
self.fit(X)
return self.transform(X)
def transform(self, X):
return X.apply(lambda x: self.led[x.name].transform(x.apply(lambda e: e if e is not None else "None")))
def inverse_transform(self, X):
return X.apply(lambda x: self.led[x.name].inverse_transform(x))
Uses Example
df = pd.DataFrame({
'pets': ['cat', 'dog', 'cat', 'monkey', 'dog', 'dog'],
'owner': ['Champ', 'Ron', 'Brick', None, 'Veronica', 'Ron'],
'location': ['San_Diego', 'New_York', 'New_York', 'San_Diego', 'San_Diego',
None]
})
print(df)
location owner pets
0 San_Diego Champ cat
1 New_York Ron dog
2 New_York Brick cat
3 San_Diego None monkey
4 San_Diego Veronica dog
5 None Ron dog
le = MultiColumnLabelEncoder()
le.fit(df)
transformed = le.transform(df)
print(transformed)
location owner pets
0 2 1 0
1 0 3 1
2 0 0 0
3 2 2 2
4 2 4 1
5 1 3 1
inverted = le.inverse_transform(transformed)
print(inverted)
location owner pets
0 San_Diego Champ cat
1 New_York Ron dog
2 New_York Brick cat
3 San_Diego None monkey
4 San_Diego Veronica dog
5 None Ron dog
This function takes a column from a dataframe and return the column where only non-NaNs are label encoded, the rest remains untouched
import pandas as pd
from sklearn.preprocessing import LabelEncoder
def label_encode_column(col):
nans = col.isnull()
nan_lst = []
nan_idx_lst = []
label_lst = []
label_idx_lst = []
for idx, nan in enumerate(nans):
if nan:
nan_lst.append(col[idx])
nan_idx_lst.append(idx)
else:
label_lst.append(col[idx])
label_idx_lst.append(idx)
nan_df = pd.DataFrame(nan_lst, index=nan_idx_lst)
label_df = pd.DataFrame(label_lst, index=label_idx_lst)
label_encoder = LabelEncoder()
label_df = label_encoder.fit_transform(label_df.astype(str))
label_df = pd.DataFrame(label_df, index=label_idx_lst)
final_col = pd.concat([label_df, nan_df])
return final_col.sort_index()
This is how I did it:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
UNKNOWN_TOKEN = '<unknown>'
a = pd.Series(['A','B','C', 'D','A'], dtype=str).unique().tolist()
a.append(UNKNOWN_TOKEN)
le = LabelEncoder()
le.fit_transform(a)
embedding_map = dict(zip(le.classes_, le.transform(le.classes_)))
and when applying to new test data:
test_df = test_df.apply(lambda x: x if x in embedding_map else UNKNOWN_TOKEN)
le.transform(test_df)
I also wanted to contribute my workaround, as I found the others a bit more tedious when working with categorical data which contains missing values
# Create a random dataframe
foo = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
# Randomly intersperse column 'A' with missing data (NaN)
foo['A'][np.random.randint(0,len(foo), size=20)] = np.nan
# Convert this series to string, to simulate our problem
series = foo['A'].astype(str)
# np.nan are converted to the string "nan", mask these out
mask = (series == "nan")
# Apply the LabelEncoder to the unmasked series, replace the masked series with np.nan
series[~mask] = LabelEncoder().fit_transform(series[~mask])
series[mask] = np.nan
foo['A'] = series
This is my attempt!
import numpy as np
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
#Now lets encode the incomplete Cabin feature
titanic_train_le['Cabin'] = le.fit_transform(titanic_train_le['Cabin'].astype(str))
#get nan code for the cabin categorical feature
cabin_nan_code=le.transform(['nan'])[0]
#Now, retrieve the nan values in the encoded data
titanic_train_le['Cabin'].replace(cabin_nan_code,np.nan,inplace=True)
I just created my own encoder which can encode a dataframe at once.
Using this class, None is encoded to 0. It can be handy when trying to make sparse matrix.
Note that the input dataframe must include categorical columns only.
class DF_encoder():
def __init__(self):
self.mapping = {None : 0}
self.inverse_mapping = {0 : None}
self.all_keys =[]
def fit(self,df:pd.DataFrame):
for col in df.columns:
keys = list(df[col].unique())
self.all_keys += keys
self.all_keys = list(set(self.all_keys))
for i , item in enumerate(start=1 ,iterable=self.all_keys):
if item not in self.mapping.keys():
self.mapping[item] = i
self.inverse_mapping[i] = item
def transform(self,df):
temp_df = pd.DataFrame()
for col in df.columns:
temp_df[col] = df[col].map(self.mapping)
return temp_df
def inverse_transform(self,df):
temp_df = pd.DataFrame()
for col in df.columns:
temp_df[col] = df[col].map(self.inverse_mapping)
return temp_df
I faced the same problem but none of the above worked for me. So I added a new row to the training data consisting only "nan"
Related
I have a dataframe of patients and their gene expressions. I has this format:
Patient_ID | gene1 | gene2 | ... | gene10000
p1 0.142 0.233 ... bla
p2 0.243 0.243 ... -0.364
...
p4000 1.423 bla ... -1.222
As you see, that dataframe contains noise, with cells that are values other then a float value.
I want to remove every row that has a any column with non numeric values.
I've managed to do this using apply and pd.to_numeric like this:
cols = df.columns[1:]
df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
df = df.dropna()
The problem is that it's taking for ever to run, and I need a better and more efficient way of achieving this
EDIT: To reproduce something like my data:
arr = np.random.random_sample((3000,10000))
df = pd.DataFrame(arr, columns=['gene' + str(i) for i in range(10000)])
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(10000)], columns=['Patient_ID']),df],axis = 1)
df['gene0'][2] = 'bla'
df['gene9998'][4] = 'bla'
Was right it is worth trying numpy :)
I got 30-60x times faster version (bigger array, larger improvement)
Convert to numpy array (.values)
Iterate through all rows
Try to convert each row to row of floats
If it fails (some NaN present), note this in boolean array
Create array based on the results
Code:
import pandas as pd
import numpy as np
from line_profiler_pycharm import profile
def op_version(df):
cols = df.columns[1:]
df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
return df.dropna()
def np_version(df):
keep = np.full(len(df), True)
for idx, row in enumerate(df.values[:, 1:]):
try:
row.astype(np.float)
except:
keep[idx] = False
pass # maybe its better to store to_remove list, depends on data
return df[keep]
#profile
def main():
arr = np.random.random_sample((3000, 5000))
df = pd.DataFrame(arr, columns=['gene' + str(i) for i in range(5000)])
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(3000)],
columns=['Patient_ID']), df], axis=1)
df['gene0'][2] = 'bla'
df['gene998'][4] = 'bla'
df2 = df.copy()
df = op_version(df)
df2 = np_version(df2)
Note I decreased number of columns so it is more feasible for tests.
Also, fixed small bug in your example, instead of:
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(10000)], columns=['Patient_ID']),df],axis = 1)
I think should be
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(3000)], columns=['Patient_ID']),df],axis = 1)
My dataset(i.e. dataset_train) has 43 categorical features, and I want to apply LabelEncoder in each of the columns.
Here i got all the categorical features:categorical_features = [features for features in dataset_train.columns if dataset_train[features].dtype == 'O']
Now a class is created to perform multiple label encoding:
class MultiColumnLabelEncoder:
def __init__(self,columns = None):
self.columns = columns # array of column names to encode
def fit(self,X,y=None):
return self # not relevant here
def transform(self,X):
output = X.copy()
if self.columns is not None:
for col in self.columns:
output[col] = LabelEncoder().fit_transform(output[col])
else:
for colname,col in output.iteritems():
output[colname] = LabelEncoder().fit_transform(col)
return output
def fit_transform(self,X,y=None):
return self.fit(X,y).transform(X)
Now after executing:
from sklearn.preprocessing import LabelEncoder
dataset_train = MultiColumnLabelEncoder(columns = categorical_features).fit_transform(dataset_train)
It's showing me :
TypeError: argument must be a string or number
Note that - NaN values are also handled previously.
how to handle this situation??
Ok, it is solved.
One of my categorical_features had just one NaN value in it.
Previously I wrote this below code to get my categorical_features containing NaN values:
categorical_features = [ feature for feature in dataset_train.columns if dataset_train[feature].isnull().sum() > 1 and dataset_train[feature].dtype == 'O' ]
That's why I got those features which had more than 1 missing values in its column.
That's why I faced this error.
Now, after I modified the above code to:
categorical_features = [ feature for feature in dataset_train.columns if dataset_train[feature].isnull().sum() >= 1 and dataset_train[feature].dtype == 'O' ]
Now I got every column even with single NaN value.
It can execute the above code for MultiColumnLabelEncoder which I mentioned in my question.
Thanks for cooperation ♥️.
Hi I'm trying to encode a Genome, stored as a string inside a dataframe read from a CSV.
Right now I'm looking to split each string in the dataframe under the column 'Genome' into a list of it's base pairs i.e. from ('acgt...') to ('a','c','g','t'...) then convert each base pair into a float (0.25,0.50,0.75,1.00) respectively.
I thought I was looking for a split function to split each string into characters but none seem to work on the data in the dataframe even when transformed to string using .tostring
Here's my most recent code:
import re
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
def string_to_array(my_string):
my_string = my_string.lower()
my_string = re.sub('[^acgt]', 'z', my_string)
my_array = np.array(list(my_string))
return my_array
label_encoder = LabelEncoder()
label_encoder.fit(np.array(['a','g','c','t','z']))
def ordinal_encoder(my_array):
integer_encoded = label_encoder.transform(my_array)
float_encoded = integer_encoded.astype(float)
float_encoded[float_encoded == 0] = 0.25 # A
float_encoded[float_encoded == 1] = 0.50 # C
float_encoded[float_encoded == 2] = 0.75 # G
float_encoded[float_encoded == 3] = 1.00 # T
float_encoded[float_encoded == 4] = 0.00 # anything else, z
return float_encoded
dfpath = 'C:\\Users\\CAAVR\\Desktop\\Ison.csv'
dataframe = pd.read_csv(dfpath)
df = ordinal_encoder(string_to_array(dataframe[['Genome']].values.tostring()))
print(df)
I've tried making my own function but I don't have any clue how they work. Everything I try points to not being able to process data when it's in a numpy array and nothing is working to transform the data to another type.
Thanks for the tips!
Edit: Here is the print of the dataframe-
Antibiotic ... Genome
0 isoniazid ... ccctgacacatcacggcgcctgaccgacgagcagaagatccagctc...
1 isoniazid ... gggggtgctggcggggccggcgccgataaccccaccggcatcggcg...
2 isoniazid ... aatcacaccccgcgcgattgctagcatcctcggacacactgcacgc...
3 isoniazid ... gttgttgttgccgagattcgcaatgcccaggttgttgttgccgaga...
4 isoniazid ... ttgaccgatgaccccggttcaggcttcaccacagtgtggaacgcgg...
There are 5 columns 'Genome' being the 5th in the list I don't know why 1. .head() will not work and 2. why print() doesn't give me all columns...
I don't think LabelEncoder is what you want. This is a simple transformation, I recommend doing it directly. Start with a lookup your base pair mapping:
lookup = {
'a': 0.25,
'g': 0.50,
'c': 0.75,
't': 1.00
# z: 0.00
}
Then apply the lookup to value of the "Genome" column. The values attribute will return the resulting dataframe as an ndarray.
dataframe['Genome'].apply(lambda bps: pd.Series([lookup[bp] if bp in lookup else 0.0 for bp in bps.lower()])).values
I have a dataframe that contains Numerical, categorical and NaN values.
customer_class B C
0 OM1 1 2.0
1 NaN 6 1.0
2 OM1 9 NaN
....
I need a LabelEncoder that keeps my missing values as 'NaN' to use an Imputer afterwards.
So I have would like to use this code in order to encode my dataframe by keeping NaN value .
here is the code :
class LabelEncoderByCol(BaseEstimator, TransformerMixin):
def __init__(self,col):
#List of column names in the DataFrame that should be encoded
self.col = col
#Dictionary storing a LabelEncoder for each column
self.le_dic = {}
for el in self.col:
self.le_dic[el] = LabelEncoder()
def fit(self,x,y=None):
#Fill missing values with the string 'NaN'
x[self.col] = x[self.col].fillna('NaN')
for el in self.col:
#Only use the values that are not 'NaN' to fit the Encoder
a = x[el][x[el]!='NaN']
self.le_dic[el].fit(a)
return self
def transform(self,x,y=None):
#Fill missing values with the string 'NaN'
x[self.col] = x[self.col].fillna('NaN')
for el in self.col:
#Only use the values that are not 'NaN' to fit the Encoder
a = x[el][x[el]!='NaN']
#Store an ndarray of the current column
b = x[el].get_values()
#Replace the elements in the ndarray that are not 'NaN'
#using the transformer
b[b!='NaN'] = self.le_dic[el].transform(a)
#Overwrite the column in the DataFrame
x[el]=b
#return the transformed D
col = data1['customer_class']
LabelEncoderByCol(col)
LabelEncoderByCol.fit(x=col,y=None)
But I got this error :
846 if mask.any():
--> 847 raise ValueError('%s not contained in the index' % str(key[mask]))
848 self._set_values(indexer, value)
849
ValueError: ['OM1' 'OM1' 'OM1' ... 'other' 'EU' 'EUB'] not contained in the index
Any idea please to resolve this error?
thanks
Two things jumped out to me when I tried to reproduce:
Your code seems to expect a dataframe will be passed to your class. But in your example you passed a series. I fixed this by wrapping the series as a dataframe before passing it to your class: col = pd.DataFrame(data1['customer_class']).
In your class' __init__ method it seemed like you had intended to iterate through a list of column names, but instead were actually iterating through all of your columns, series by series. I fixed this by changing the appropriate line to: self.col = col.columns.values.
Below, I've pasted in my modifications to your class' __init__ and fit methods (my only modification to the transform method was to have it return the modified dataframe):
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder
data1 = pd.DataFrame({'customer_class': ['OM1', np.nan, 'OM1'],
'B': [1,6,9],
'C': [2.0, 1.0, np.nan]})
class LabelEncoderByCol(BaseEstimator, TransformerMixin):
def __init__(self,col):
#List of column names in the DataFrame that should be encoded
self.col = col.columns.values
#Dictionary storing a LabelEncoder for each column
self.le_dic = {}
for el in self.col:
self.le_dic[el] = LabelEncoder()
def fit(self,x,y=None):
#Fill missing values with the string 'NaN'
x = x.fillna('NaN')
for el in self.col:
#Only use the values that are not 'NaN' to fit the Encoder
a = x[el][x[el]!='NaN']
self.le_dic[el].fit(a)
return self
def transform(self,x,y=None):
#Fill missing values with the string 'NaN'
x[self.col] = x[self.col].fillna('NaN')
for el in self.col:
#Only use the values that are not 'NaN' to fit the Encoder
a = x[el][x[el]!='NaN']
#Store an ndarray of the current column
b = x[el].get_values()
#Replace the elements in the ndarray that are not 'NaN'
#using the transformer
b[b!='NaN'] = self.le_dic[el].transform(a)
#Overwrite the column in the DataFrame
x[el]=b
return x
I am able to run the following lines (also slightly modified from your initial implementation) with no error:
col = pd.DataFrame(data1['customer_class'])
lenc = LabelEncoderByCol(col)
lenc.fit(x=col,y=None)
I can then access the classes for the customer_class column from your example:
lenc.fit(x=col,y=None).le_dic['customer_class'].classes_
Which outputs:
array(['OM1'], dtype=object)
Finally, I can transform the column using your class' transform method:
lenc.transform(x=col,y=None)
Which outputs the following:
customer_class
0 0
1 NaN
2 0
Learning Python here, and any help on this is much appreciated.
My problem scenario is, there are 2 dataframes A and B contains a column(Name and Flag) list of Names.
ExDF = pd.DataFrame({'Name' : ['Smith','John, Alex','Peter Lin','Carl Marx','Abhraham Moray','Calvin Klein'], 'Flag':['False','False','False','False','False','False']})
SnDF = pd.DataFrame({'Name' : ['Adam K ','John Smith','Peter Lin','Carl Josh','Abhraham Moray','Tim Klein'], 'Flag':['False','False','False','False','False','False']})
The initial value of Flag is False.
Point 1: I need to flip the names in both dataframe ie. Adam Smith to Smith Adam and save the flip names in another new column in the both dataframes.
- This part is done.
Point 2: Then both the Original name and flip names of A dataframe should get check in B dataframe original names and flip names. If it found the the flag column in both the dataframe should get update by True.
I wrote the code but it checks one on one row to both dataframe like A[0] to B[0], A[1] to B[1], but i need to check A[0] record to all the records of B dataframe.
Pls help me on this!!
The code which tried is below:
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
ExDF_swap = ExDF["Swap"] = ExDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
SnDF_swap = SnDF["Swap"] = SnDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
ExDF_swap = pd.DataFrame(ExDF_swap)
SnDF_swap = pd.DataFrame(SnDF_swap)
vect = CountVectorizer()
X = vect.fit_transform(ExDF_swap.Name)
Y = vect.transform(SnDF_swap.Name)
res = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
pd.DataFrame(X.toarray(), columns=vect.get_feature_names())
pd.DataFrame(Y.toarray(), columns=vect.get_feature_names())
ExDF["Flag"] = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
SnDF["Flag"] = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
You could try isin() - of pandas:
import pandas as pd
ExDF = pd.DataFrame({'Name' : ['Smith','John, Alex','Peter Lin','Carl Marx','Abhraham Moray','Calvin Klein'], 'Flag':['False','False','False','False','False','False']})
SnDF = pd.DataFrame({'Name' : ['Adam K ','John Smith','Peter Lin','Carl Josh','Abhraham Moray','Tim Klein'], 'Flag':['False','False','False','False','False','False']})
print(ExDF)
print(SnDF)
ExDF["Swap"] = ExDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
SnDF["Swap"] = SnDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
print(ExDF)
print(SnDF)
ExDF['Flag'] = ExDF.Name.isin(SnDF.Name)
SnDF['Flag'] = SnDF.Name.isin(ExDF.Name)
print(ExDF)
print(SnDF)