CODEWITHSUNDEEP
pythoncsvpandas
e9e9s
e9e9s

Reputation: 955

How to keep pandas indexes when extracting columns

So I have a file 500 columns by 600 rows and want to take the average of all columns for rows 200-400:

df = pd.read_csv('file.csv', sep= '\s+')
sliced_df=df.iloc[200:400]

Then create a new column of the averages of all rows across all columns. And extract only that newly created column:

sliced_df['mean'] = sliced_df.mean(axis=1)
final_df = sliced_df['mean']

But how can I prevent the indexes from resetting when I extract the new column?

Upvotes: 1

Views: 513

Answers (2)

jezrael
jezrael

Reputation: 863361

I think is not necessary create new column in sliced_df, only rename name of Series and if need output as DataFrame add to_frame. Indexes are not resetting, see sample bellow:

#random dataframe
np.random.seed(100)
df = pd.DataFrame(np.random.randint(10, size=(5,5)), columns=list('ABCDE'))
print (df)
   A  B  C  D  E
0  8  8  3  7  7
1  0  4  2  5  2
2  2  2  1  0  8
3  4  0  9  6  2
4  4  1  5  3  4

#in real data use df.iloc[200:400]
sliced_df=df.iloc[2:4]
print (sliced_df)
   A  B  C  D  E
2  2  2  1  0  8
3  4  0  9  6  2

final_ser = sliced_df.mean(axis=1).rename('mean')
print (final_ser)
2    2.6
3    4.2
Name: mean, dtype: float64

final_df = sliced_df.mean(axis=1).rename('mean').to_frame()
print (final_df)
   mean
2   2.6
3   4.2

Python counts from 0, so maybe need change slice from 200:400 to 100:300, see difference:

sliced_df=df.iloc[1:3]
print (sliced_df)
   A  B  C  D  E
1  0  4  2  5  2
2  2  2  1  0  8

final_ser = sliced_df.mean(axis=1).rename('mean')
print (final_ser)
1    2.6
2    2.6
Name: mean, dtype: float64

final_df = sliced_df.mean(axis=1).rename('mean').to_frame()
print (final_df)
   mean
1   2.6
2   2.6

Upvotes: 1

Mahesh
Mahesh

Reputation: 141

Use copy() function as follows: 

df = pd.read_csv('file.csv', sep= '\s+')
sliced_df=df.iloc[200:400].copy()
sliced_df['mean'] = sliced_df.mean(axis=1)
final_df = sliced_df['mean'].copy()

Upvotes: 0

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