Drop a pandas DataFrame row that comes after a row that contains a particular value

Question

I am trying to drop all rows that come after a row which has yes inside the 'Ammend' column

df:

  Ammend
 0  no
 1  yes
 2  no
 3  no
 4  yes
 5  no

Required output df:

  Ammend
 0  no
 1  yes
 3  no
 4  yes

Look at the following code:

df = df.drop(df[df['Amended' == 'yes']], inplace=True)

Returns a KeyError: False error message

I have tried many different variations of this using different methods like .index.tolist() and .loc but I can't seem to figure it out anyway.

I have also tried truncate:

filings_df.truncate(after=filings_df.loc[filings_df['Filings'] == '10-K/A'].index[0], before = filings_df.loc[filings_df['Filings'] == '10-K/A'].index[1])

This returns:

IndexError: index 1 is out of bounds for axis 0 with size 1

Answer 1

Try this

import pandas as pd
import numpy as np

np.random.seed(525)
df = pd.DataFrame({'Other': np.random.rand(10), 'Ammend': np.random.choice(['yes', 'no'], 10)})
df

      Other Ammend
0  0.750282     no
1  0.379455     no
2  0.766467    yes
3  0.351025     no
4  0.965993     no
5  0.709159     no
6  0.838831    yes
7  0.218321     no
8  0.573360    yes
9  0.738974     no

Output:

df.drop(index=df[df['Ammend'].shift() == 'yes'].index)

      Other Ammend
0  0.750282     no
1  0.379455     no
2  0.766467    yes
4  0.965993     no
5  0.709159     no
6  0.838831    yes
8  0.573360    yes

Answer 2

One way using pandas.Series.ne with shift trick:

s = df["Ammend"]
new_df = df[~s.ne(s.shift()).cumsum().duplicated(keep="first")]
print(new_df)

Output:

  Ammend
0     no
1    yes
2     no
4    yes
5     no