Menu
I have the following DataFrame:
| no | word | status | |
|---|---|---|---|
| 0 | 0 | one | to_check |
| 1 | 1 | two | to_check |
| 2 | 2 | :) | emoticon |
| 3 | 3 | dr. | to_check |
| 4 | 4 | "future" | to_check |
| 5 | 5 | to | to_check |
| 6 | 6 | be | to_check |
I want to iterate trough each row to find quotes at word initial and final positions and create a DataFrame like this:
| no | word | status | |
|---|---|---|---|
| 0 | 0 | one | to_check |
| 1 | 1 | two | to_check |
| 2 | 2 | :) | emoticon |
| 3 | 3 | dr. | to_check |
| 4 | 4 | " | quotes |
| 5 | 4 | future | word |
| 6 | 4 | " | quotes |
| 7 | 5 | to | to_check |
| 8 | 6 | be | to_check |
I can strip quotes and split the word into three pieces but I got the this DataFrame, it overwrites the last two rows:
| no | word | status | |
|---|---|---|---|
| 0 | 0 | one | to_check |
| 1 | 1 | two | to_check |
| 2 | 2 | :) | emoticon |
| 3 | 3 | dr. | to_check |
| 4 | 4 | " | quotes |
| 5 | 4 | future | word |
| 6 | 4 | " | quotes |
I tried df.loc[index], df.iloc[index], df.at[index] but none of them helped me to extend the number of rows in the DataFrame.
Is it possible to add new rows at specific index without overwriting last two rows?
835
To split cell into multiple rows in a Python Pandas dataframe, we can use the apply method. to call apply with a lambda function that calls str. split to split the x string value. And then we call explode to fill new rows with the split values.
split() Pandas provide a method to split string around a passed separator/delimiter. After that, the string can be stored as a list in a series or it can also be used to create multiple column data frames from a single separated string.
Pandas DataFrame - expanding() functionThe expanding() function is used to provide expanding transformations. Minimum number of observations in window required to have a value (otherwise result is NA). Set the labels at the center of the window.
You can use the following basic syntax to split a string column in a pandas DataFrame into multiple columns: #split column A into two columns: column A and column B df[[' A ', ' B ']] = df[' A ']. str. split (', ', 1, expand= True) The following examples show how to use this syntax in practice. Example 1: Split Column by Comma
If you have your own data to follow along with, feel free to do so (though your results will, of course, vary): We have four records and three different columns, covering a person’s Name, Age, and Location. The easiest way to add or insert a new row into a Pandas DataFrame is to use the Pandas .append () method.
Pandas >= 0.25 Series and DataFrame methods define a.explode () method that explodes lists into separate rows. See the docs section on Exploding a list-like column. Since you have a list of comma separated strings, split the string on comma to get a list of elements, then call explode on that column.
Column order and names are retained. def tidy_split(df, column, sep='|', keep=False): """ Split the values of a column and expand so the new DataFrame has one split value per row. Filters rows where the column is missing.
In your case you can split then explode
out = df.assign(word = df.word.str.split(r'(\")')).explode('word').\
loc[lambda x : x['word']!='']
no word status
0 0 one to_check
1 1 two to_check
2 2 :) emoticon
3 3 dr. to_check
4 4 " to_check
4 4 future to_check
4 4 " to_check
5 5 to to_check
6 6 be to_check
For change the status
out['status'] = np.where(out['word'].eq('"'), 'quotes',out['status'])
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
