Groupby in Reverse

You can use index.repeat:

i = df.index.repeat(df['count'])
d = df.loc[i, :'value'].reset_index(drop=True)

---

   var  value
0   A     10
1   B     20
2   B     20
3   C     30
4   C     30
5   C     30

Use repeat with reindex for this short one-liner:

df.reindex(df.index.repeat(df['count']))

Output:

  var  value  count
0   A     10      1
1   B     20      2
1   B     20      2
2   C     30      3
2   C     30      3
2   C     30      3

Or to eliminate the 'count' column:

df[['var','value']].reindex(df.index.repeat(df['count']))

OR

df.reindex(df.index.repeat(df['count'])).drop('count', axis=1)

Output:

  var  value
0   A     10
1   B     20
1   B     20
2   C     30
2   C     30
2   C     30

Using Series.repeat

import pandas as pd
df = pd.DataFrame({'var':['A', 'B', 'C'], 'value':[10, 20, 30], 'count':[1,2,3]})
new_df = pd.DataFrame()

new_df['var'] = df['var'].repeat(df['count'])
new_df['value'] = df['value'].repeat(df['count'])
new_df

    var value
0   A   10
1   B   20
1   B   20
2   C   30
2   C   30
2   C   30