For loop in pandas dataframe using enumerate

Question

I have a basic dataframe which is a result of a gruopby from unclean data:

 df:

Name1   Value1  Value2
A       10      30
B       40      50

I have created a list as follows:

Segment_list = df['Name1'].unique()
Segment_list 

array(['A', 'B'], dtype=object)

Now i want to traverse the list and find the amount in Value1 for each iteration so i am usinig:

for Segment_list in enumerate(Segment_list):
    print(df['Value1'])

But I getting both values instead of one by one. I just need one value for one iteration. Is this possible?

Expected output:

10
40

Answer 1

• I recommend using pandas.DataFrame.groupby to get the values for each group.
• For the most part, using a for-loop with pandas is an indication that it's probably not being done correctly or efficiently.
• Additional resources:
  • Fast, Flexible, Easy and Intuitive: How to Speed Up Your Pandas Projects
  • Stack Overflow Pandas Tag Info Page

Option 1:

import pandas as pd
import numpy as np
import random

np.random.seed(365)
random.seed(365)
rows = 25
data = {'n': [random.choice(['A', 'B', 'C']) for _ in range(rows)],
        'v1': np.random.randint(40, size=(rows)),
        'v2': np.random.randint(40, size=(rows))}

df = pd.DataFrame(data)

# groupby n
for g, d in df.groupby('n'):
#     print(g)               # use or not, as needed
    print(d.v1.values[0])    # selects the first value of each group and prints it

[out]:  # first value of each group
5
33
18

Option 2:

dfg = df.groupby(['n'], as_index=False).agg({'v1': list})

# display(dfg)
   n                                   v1
0  A  [5, 26, 39, 39, 10, 12, 13, 11, 28]
1  B      [33, 34, 28, 31, 27, 24, 36, 6]
2  C        [18, 27, 9, 36, 35, 30, 3, 0]

Option 3:

• As stated in the comments, your data is already the result of groupby , and it will only ever have one value in the column for each group.

dfg = df.groupby('n', as_index=False).sum()

# display(dfg)

   n   v1   v2
0  A  183  163
1  B  219  188
2  C  158  189

# print the value for each group in v1
for v in dfg.v1.to_list():
    print(v)

[out]:
183
219
158

Option 4:

• Print all rows for each column

dfg = df.groupby('n', as_index=False).sum()

for col in dfg.columns[1:]:  # selects all columns after n
    for v in dfg[col].to_list():
        print(v)

[out]:
183
219
158
163
188
189

Answer 2

I agree with @Trenton's comment that the whole point of using data frames is to avoid looping through them like this. Re-think this using a function. However the closest way to make what you've written work is something like this:

Segment_list = df['Name1'].unique()
for Index in Segment_list:
    print(df['Value1'][df['Name1']==Index]).iloc[0]

Depending on what you want to happen if there are two entries for Name (presumably this can happen because you use .unique() , This will print the sum of the Values:

df.groupby('Name1').sum()['Value1']