I have a dataframe that consists of a header of a variable number of lines, then it has several columns of numeric data
The dataframe looks something like this (I've skipped a bunch of rows, but it's basically the same):
| Index | Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 |
|---|---|---|---|---|---|---|
| 0 | NaN | Job No: 21-05-23384 | NaN | NaN | NaN | |
| 1 | NaN | Client: Bob Canada | NaN | NaN | NaN | |
| 2 | NaN | Project: Uncle 3 | NaN | NaN | NaN | |
| 3 | NaN | Sounding ID: SCPT21 | NaN | NaN | NaN | |
| 4 | NaN | Date: 04-Dec-2021 | NaN | NaN | NaN | |
| 14 | Depth (m) | Header | Header | Header | Header | |
| 15 | 0.92 | 0.90 | NaN | NaN | NaN | |
| 16 | 1.92 | 1.90 | 245 | 1.34 | 55 |
All I want to get is the index of the row the first value is in, 15. I'm not sure how to do it. Thanks.
CodePudding user response：
In my mind, the most logical way is to treat the Unnamed: 0 column rows as strings, return the first character of each string and determine if the character is numeric or not with isnumeric(). This will return either True or False in a new column.
df['numeric'] = df['Unnamed: 0'].astype(str).str[0].str.isnumeric()
Next, I would take all the indexes where the numeric column is True, assign the index values to a list and take the first value from that list:
idx = df.index[df['numeric']].tolist()[0]