How to Add Incremental Numbers to a New Column Using Pandas

How to Add Incremental Numbers to a New Column Using Pandas

Here:

df = df.reset_index()
df = df.rename(columns={"index":"New_ID"})
df['New_ID'] = df.index + 880

Pandas creating incremental values in new column based on certain conditions

One approach:

ranks = df.sort_values(by=["Rank"],
                    key=lambda x: x.str.replace(r"\D+", "", regex=True).astype(int))\
        .groupby("Name").transform("cumcount") + 1
ranks = ranks.apply("P{}".format)

df["NewIncremental"] = ranks
print(df)

Output

   Name Rank  Months NewIncremental
0     A   A1       2             P1
1     A   A1       2             P2
2     A   A2       3             P3
3     A   A2       3             P4
4     A   A2       3             P5
5     B   A1       4             P1
6     B   A1       4             P2
7     B   A1       4             P3
8     B   A1       4             P4
9     C   A3       2             P2
10    C   A3       2             P3
11    C   A2       1             P1

Step-by-step

# sort df by the given criteria, then group-by
sorted_by_rank = df.sort_values(by=["Rank"], key=lambda x: x.str.replace(r"\D+", "", regex=True).astype(int))

# get the ranks and apply the expected format
ranks = sorted_by_rank.groupby("Name").transform("cumcount") + 1
ranks = ranks.apply("P{}".format)

# assign the new column
df["NewIncremental"] = ranks
print(df)

Pandas Dataframe - Generate incremental values

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

How to increment number in pandas column from bottom to top on certain conditions

Let df is your DataFrame. Observed that, data is already sorted by SR.No and Date columns.

threshold_date = '01-03-2015'
df['Date'] = pd.to_datetime(df['Date'])
starters = df[df['Date'] == threshold_date].index
df.loc[starters, 'Res'] = 1
j = 0
for i in starters:
    _index = list(range(2, i+2-j))
    _index.reverse()
    df.loc[j:i-1, 'Res'] = _index
    j = i+1
df.loc[df['Date'] > '01-03-2015', 'Res'] = 0

It will produce your required output

create new column of incremental number based on 2 categorical columns pandas dataframe

You can use cumcount after groupby on username and phase.

df['count'] = df.groupby(['username', 'phase']).cumcount()+1
print(df)

---

  username  phase  count
0   andrew      1      1
1   andrew      1      2
2     alex      1      1
3     alex      2      1
4   andrew      1      3
5    cindy      3      1
6     alex      2      2

Add incremeting number to certain coulmns of a pandas DataFrame

If your category column is sorted, we can use GroupBy.cumcount:

df['incrNbr'] = df.groupby('category')['category'].cumcount().add(1)

     val category  incrNbr
idx                       
9     30        a        1
8     40        a        2
7     50        b        1
6     60        b        2
5     70        c        1
4     80        c        2
3     90        c        3

Pandas Add an incremental number based on another column

Try with diff then cumsum

df['run'] = df['sequence'].diff().ne(1).cumsum()
Out[349]: 
0     1
1     1
2     1
3     1
4     1
5     2
6     2
7     2
8     3
9     3
10    3
11    3
12    3
13    3
14    3
Name: sequence, dtype: int32

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