I am working with Python and have a series which is as follows:
view_count comment_count like_count dislike_count ratio_of_comments_per_view ratio_of_likes_per_view
count 2.200000e+01 21.000000 22.000000 22.000000 21.000000 22.000000
mean 1.481812e+06 4547.523810 49981.863636 667.136364 0.002539 0.037818
std 2.263283e+06 8716.083952 79607.504617 1249.618086 0.001072 0.010861
After count, mean and std categories, I need a new row called number of entries which include number of entries for each group (number of entries for view count, number of entries for comment count etc.). Actually I could get number of entries by using .info()
option and it gave me the following results:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 22 entries, 2 to 67
Data columns (total 8 columns):
title 22 non-null object
view_count 22 non-null int64
comment_count 21 non-null float64
like_count 22 non-null int64
dislike_count 22 non-null int64
ratio_of_comments_per_view 21 non-null float64
ratio_of_likes_per_view 22 non-null float64
other_tag 22 non-null object
dtypes: float64(3), int64(3), object(2)
memory usage: 1.5+ KB
But I do not know how to add these number of entries as a new row in my series. Is there anyone who can help me with this problem?
My series should look like this:
view_count comment_count like_count dislike_count ratio_of_comments_per_view ratio_of_likes_per_view
count 2.200000e+01 21.000000 22.000000 22.000000 21.000000 22.000000
mean 1.481812e+06 4547.523810 49981.863636 667.136364 0.002539 0.037818
std 2.263283e+06 8716.083952 79607.504617 1249.618086 0.001072 0.010861
#entries 22 21 22 22 21 22
We can use DataFrame.count
:
For each column/row the number of non-NA/null entries.
If you want count by columns and add a new row:
df=df.append(df.count().to_frame('entries').T)
print(df)
output for the sample dataframe:
view_count comment_count like_count dislike_count \
count 22.0 21.000000 22.000000 22.000000
mean 1481812.0 4547.523810 49981.863636 667.136364
std 2263283.0 8716.083952 79607.504617 1249.618086
entries 3.0 3.000000 3.000000 3.000000
ratio_of_comments_per_view ratio_of_likes_per_view
count 21.000000 22.000000
mean 0.002539 0.037818
std 0.001072 0.010861
entries 3.000000 3.000000
If you want count by rows and create a new column:
df['entries']=df.count(axis=1)
print(df)
Output:
view_count comment_count like_count dislike_count \
count 22.0 21.000000 22.000000 22.000000
mean 1481812.0 4547.523810 49981.863636 667.136364
std 2263283.0 8716.083952 79607.504617 1249.618086
ratio_of_comments_per_view ratio_of_likes_per_view entries
count 21.000000 22.000000 6
mean 0.002539 0.037818 6
std 0.001072 0.010861 6