Print row values as lists iteratively without getting the column names
Question:
I am trying to get rows of a dataframe as separate lists. This list should only contain the values in a certain row for a certain number of rows. For instance, a dataframe that looks like the following should print out only the rows as lists one after the other:
Example dataframe
Name
Age
Gender
Michael
5
M
Fitz
10
F
should print out
[Michael, 5, M]
[Fitz, 10, F]
I know df.loc[[1]] prints out the first row, but it also prints out the column names. I am trying to get rid of the column names, and have a list of only row values.
Answers:
Depending on what you can, you can join the rows into list with DataFrame.apply or just convert the DataFrame to list of rows
cols = ['Name', 'Age', 'Gender']
out = df[cols].apply(list, axis=1)
# or
lsts = df[cols].values.tolist()
print(out)
0 [Michael, 5, M]
1 [Fitz, 10, F]
dtype: object
print(lsts)
[['Michael', 5, 'M'], ['Fitz', 10, 'F']]
Depending on if it’s a header or regular row you have two options:
# If the first row is a header row.
df.values.tolist()
# If the first row is a regular row. Where the [1:] determines how many of the rows
# you want to remove.
df.values.tolist()[1:]
Both produce:
[['Michael', 5, 'M'], [10, 'F', 'Fitz']]
I am trying to get rows of a dataframe as separate lists. This list should only contain the values in a certain row for a certain number of rows. For instance, a dataframe that looks like the following should print out only the rows as lists one after the other:
Example dataframe
| Name | Age | Gender |
|---|---|---|
| Michael | 5 | M |
| Fitz | 10 | F |
should print out
[Michael, 5, M]
[Fitz, 10, F]
I know df.loc[[1]] prints out the first row, but it also prints out the column names. I am trying to get rid of the column names, and have a list of only row values.
Depending on what you can, you can join the rows into list with DataFrame.apply or just convert the DataFrame to list of rows
cols = ['Name', 'Age', 'Gender']
out = df[cols].apply(list, axis=1)
# or
lsts = df[cols].values.tolist()
print(out)
0 [Michael, 5, M]
1 [Fitz, 10, F]
dtype: object
print(lsts)
[['Michael', 5, 'M'], ['Fitz', 10, 'F']]
Depending on if it’s a header or regular row you have two options:
# If the first row is a header row.
df.values.tolist()
# If the first row is a regular row. Where the [1:] determines how many of the rows
# you want to remove.
df.values.tolist()[1:]
Both produce:
[['Michael', 5, 'M'], [10, 'F', 'Fitz']]