CODEWITHSUNDEEP
pythonpandas
3pitt
3pitt

Reputation: 951

Make new pandas columns based on pipe-delimited column with possible repeats

This question pertains to the fine solution to my previous question, Create Multiple New Columns Based on Pipe-Delimited Column in Pandas

I have a pipe delimited column that I want to convert to multiple new columns which count the occurrence of elements in each row's pipe-string. I've been given a solution that works except for rows with empty cells in the pertinent column, where it leaves NaN/blanks instead of 0s. Besides a posteriori NaN->0 conversion, is there a way to augment the current solution?

    import pandas as pd
    import numpy as np
    df1 = pd.DataFrame(np.array([
    [1202, 2007, 99.34,None],
    [9321, 2009, 61.21,'12|34'],
    [3832, 2012, 12.32,'12|12|34'],
    [1723, 2017, 873.74,'28|13|51']]),
    columns=['ID', 'YEAR', 'AMT','PARTS'])

part_dummies = df1.PARTS.str.get_dummies().add_prefix('Part_')
print(pd.concat([df1, part_dummies], axis=1, join_axes=[df1.index]))

# Expected Output:
# ID    YEAR    AMT     PART_12     PART_34     PART_28     PART_13     PART_51
# 1202  2007    99.34   0           0           0           0           0
# 9321  2009    61.21   1           1           0           0           0
# 3832  2012    12.32   2           1           0           0           0
# 1723  2017    873.74  0           0           1           1           1

    # Actual Output:
    # ID    YEAR    AMT     PART_12     PART_34     PART_28     PART_13     PART_51
    # 1202  2007    99.34   0           0           0           0           0
    # 9321  2009    61.21   1           1           0           0           0
    # 3832  2012    12.32   1           1           0           0           0
    # 1723  2017    873.74  0           0           1           1           1

part_dummies = pd.get_dummies(df1.PARTS.str.split('|',expand=True).stack()).sum(level=0).add_prefix('Part_')
print(pd.concat([df1, part_dummies], axis=1, join_axes=[df1.index]))

# ID    YEAR    AMT     PART_12     PART_13     PART_28     PART_34     PART_51
# 1202  2007    99.34   NaN         NaN         NaN         NaN         NaN
# 9321  2009    61.21   1           0           0           1           0
# 3832  2012    12.32   2           0           0           1           0
# 1723  2017    873.74  0           1           1           0           1

Upvotes: 3

Views: 2397

Answers (3)

Surya Chhetri
Surya Chhetri

Reputation: 11568

Using this expanded version - should work too; also, will retain original columns additionally

In [728]: import pandas as pd

# Dataframe used from Mike's(data) above:
In [729]: df = pd.DataFrame(np.array([
   .....:     [1202, 2007, 99.34,None],
   .....:     [9321, 2009, 61.21,'12|34'],
   .....:     [3832, 2012, 12.32,'12|12|34'],
   .....:     [1723, 2017, 873.74,'28|13|51']]),
   .....:     columns=['ID', 'YEAR', 'AMT','PARTS'])

# quick glimpse of dataframe
In [730]: df
Out[730]: 
     ID  YEAR     AMT     PARTS
0  1202  2007   99.34      None
1  9321  2009   61.21     12|34
2  3832  2012   12.32  12|12|34
3  1723  2017  873.74  28|13|51

# expand string based on delimiter ("|")
In [731]: expand_str = df["PARTS"].str.split('|', expand=True)

# generate dummies df:
In [732]: dummies_df = pd.get_dummies(expand_str.stack(dropna=False)).sum(level=0).add_prefix("Part_")

# gives concatenated or combined df(i.e dummies_df + original df):
In [733]: pd.concat([df, dummies_df], axis=1)
Out[733]: 
     ID  YEAR     AMT     PARTS  Part_12  Part_13  Part_28  Part_34  Part_51
0  1202  2007   99.34      None        0        0        0        0        0
1  9321  2009   61.21     12|34        1        0        0        1        0
2  3832  2012   12.32  12|12|34        2        0        0        1        0
3  1723  2017  873.74  28|13|51        0        1        1        0        1

Upvotes: 0

Scott Boston
Scott Boston

Reputation: 153510

stack was dropping NaNs. Using dropna=False will solve this:

pd.get_dummies(df1.set_index(['ID','YEAR','AMT']).PARTS.str.split('|', expand=True)\
                  .stack(dropna=False), prefix='Part')\
  .sum(level=0)

Output:

      Part_12  Part_13  Part_28  Part_34  Part_51
ID                                               
1202        0        0        0        0        0
9321        1        0        0        1        0
3832        2        0        0        1        0
1723        0        1        1        0        1

Upvotes: 4

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210922

you can use sklearn.feature_extraction.text.CountVectorizer:

In [22]: from sklearn.feature_extraction.text import CountVectorizer

In [23]: cv = CountVectorizer()

In [24]: t = pd.DataFrame(cv.fit_transform(df1.PARTS.fillna('').str.replace(r'\|', ' ')).A,
    ...:                  columns=cv.get_feature_names(),
    ...:                  index=df1.index).add_prefix('PART_')
    ...:

In [25]: df1 = df1.join(t)

In [26]: df1
Out[26]:
     ID  YEAR     AMT     PARTS  PART_12  PART_13  PART_28  PART_34  PART_51
0  1202  2007   99.34      None        0        0        0        0        0
1  9321  2009   61.21     12|34        1        0        0        1        0
2  3832  2012   12.32  12|12|34        2        0        0        1        0
3  1723  2017  873.74  28|13|51        0        1        1        0        1

Upvotes: 2

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