I want to create a new named column in a Pandas dataframe, insert first value into it, and then add another values to the same column:
Something like:
import pandas
df = pandas.DataFrame()
df['New column'].append('a')
df['New column'].append('b')
df['New column'].append('c')
etc.
How do I do that?
If I understand, correctly you want to append a value to an existing column in a pandas data frame. The thing is with DFs you need to maintain a matrix-like shape so the number of rows is equal for each column what you can do is add a column with a default value and then update this value with
for index, row in df.iterrows():
df.at[index, 'new_column'] = new_value
Dont do it, because it's slow:
- updating an empty frame a-single-row-at-a-time. I have seen this method used WAY too much. It is by far the slowest. It is probably common place (and reasonably fast for some python structures), but a DataFrame does a fair number of checks on indexing, so this will always be very slow to update a row at a time. Much better to create new structures and concat.
Better to create a list of data and create DataFrame by contructor:
vals = ['a','b','c']
df = pandas.DataFrame({'New column':vals})
If in case you need to add random values to the newly created column, you could also use
df['new_column']= np.random.randint(1, 9, len(df))
