I am trying to write a quick function to compare the current cell in a column to the cell just above (before) it. The idea is to perform a different operation on data that is in the same column but different value.
if (df.loc() != df.loc[::-1] & df1.loc() != df1.loc[:-1]):
df = df.iloc()
df1 = df1.iloc()
My thinking was to compare the current location to the current location -1, and if they are the same then to proceed. Otherwise do another task when the cell value changes. I am doing this to two different data frames that have the same column name in each that I am trying to read through.
Connector Pin Adj.
0 F123 1 2 6 7
1 F123 2 1 3 6 7 8
2 F123 3 2 4 7 8 9
3 F123 4 3 5 8 9 10
4 F123 5 4 9 10
5 F123 6 1 2 7
6 F123 7 1 2 3 6 8
7 F123 8 2 3 4 7 9
8 F123 9 3 4 5 8 10
9 F123 10 4 5 9
10 C137 1 2 1
11 C137 2 1
After iterating down this table, when the Connector changes from F123 to C137 I want to clear all columns above the first C137.
Considering the below dataframe:
print(df)
Connector Pin Adj.
0 F123 1 2 6 7
1 F123 2 1 3 6 7 8
2 F123 3 2 4 7 8 9
3 F123 4 3 5 8 9 10
4 F123 5 4 9 10
5 F123 6 1 2 7
6 F123 7 1 2 3 6 8
7 F123 8 2 3 4 7 9
8 F123 9 3 4 5 8 10
9 F123 10 4 5 9
10 C137 1 2 1
11 C137 2 1
If you use:
df.drop(range(df.Connector.ne(df.Connector.shift()).cumsum().idxmax()))
Connector Pin Adj.
10 C137 1 2 1
11 C137 2 1
This will identify the change and drop those rows before where the change in connector has happen
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