I have follow simple DataFrame - df
:
0
0 1
1 2
2 3
Once I try to create a new columns and assign some values for them, as example below:
df['col2', 'col3'] = [(2,3), (2,3), (2,3)]
I got following structure
0 (col2, col3)
0 1 (2, 3)
1 2 (2, 3)
2 3 (2, 3)
However, I am looking a way to get as here:
0 col2, col3
0 1 2, 3
1 2 2, 3
2 3 2, 3
看起来解决方案很简单:
df['col2'], df['col3'] = zip(*[(2,3), (2,3), (2,3)])
There is a convenient solution to joining multiple series to a dataframe via a list of tuples. You can construct a dataframe from your list of tuples before assignment:
df = pd.DataFrame({0: [1, 2, 3]})
df[['col2', 'col3']] = pd.DataFrame([(2,3), (2,3), (2,3)])
print(df)
0 col2 col3
0 1 2 3
1 2 2 3
2 3 2 3
This is convenient, for example, when you wish to join an arbitrary number of series.
I ran across this issue when trying to apply multiple scalar values to multiple new columns and couldn't find a better way. If I'm missing something blatantly obvious, let me know, but df[['b','c']] = 0
doesn't work. but here's the simplified code:
# Create the "current" dataframe
df = pd.DataFrame({'a':[1,2]})
# List of columns I want to add
col_list = ['b','c']
# Quickly create key : scalar value dictionary
scalar_dict = { c : 0 for c in col_list }
# Create the dataframe for those columns - key here is setting the index = df.index
df[col_list] = pd.DataFrame(scalar_dict, index = df.index)
Or, what appears to be slightly faster is to use .assign()
:
df = df.assign(**scalar_dict)
alternatively assign
can be used
df.assign(col2 = 2, col3= 3)
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.