CODEWITHSUNDEEP
pythonpython-2.7pandas
cpt_python
cpt_python

Reputation: 403

Pandas Dataframe - Generate incremental values

In my workflow there are multiple CSVs with four columns OID, value, count, unique_id. I am trying to figure how to generate incremental values under unique_id column. Using apply(), I can do something like df.apply(lambda x : x + 1) #where x = 0 and it will result in all values under unique_id as 1. However, I am confused on how to use apply() to generate incrementally values in each row for a specific column. 

# Current Dataframe 
   OID  Value  Count  unique_id
0   -1      1      5          0
1   -1      2     46          0
2   -1      3     32          0
3   -1      4      3          0
4   -1      5     17          0

# Trying to accomplish
   OID  Value  Count  unique_id
0   -1      1      5          0
1   -1      2     46          1
2   -1      3     32          2
3   -1      4      3          3
4   -1      5     17          4

Sample code (I understand that the syntax is incorrect, but it is approximately what I am trying to accomplish):

def numbers():
    for index, row in RG_Res_df.iterrows():
        return index

RG_Res_df = RG_Res_df['unique_id'].apply(numbers)

Upvotes: 4

Views: 11056

Answers (1)

EdChum
EdChum

Reputation: 394159

don't loop you can just directly assign a numpy array to generate the id, here using np.arange and pass the num of rows which will be df.shape[0]

In [113]:
df['unique_id'] = np.arange(df.shape[0])
df

Out[113]:
   OID  Value  Count  unique_id
0   -1      1      5          0
1   -1      2     46          1
2   -1      3     32          2
3   -1      4      3          3
4   -1      5     17          4

or pure pandas method using RangeIndex, here the default start is 0 so we only need to pass stop=df.shape[0]:

In [114]:
df['unique_id'] = pd.RangeIndex(stop=df.shape[0])
df

Out[114]:
   OID  Value  Count  unique_id
0   -1      1      5          0
1   -1      2     46          1
2   -1      3     32          2
3   -1      4      3          3
4   -1      5     17          4

Upvotes: 7

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