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Is there any function like the following to create a dataframe with ten columns of Series s?
df = pd.DataFrame(s, 10)
Thank you!
886
You can create a DataFrame from multiple Series objects by adding each series as a columns. By using concat() method you can merge multiple series together into DataFrame. This takes several params, for our scenario we use list that takes series to combine and axis=1 to specify merge series as columns instead of rows.
To create a data frame with a column having repeated values, we simply need to use rep function and we can repeat the values in a sequence of the values passed or repeating each value a particular number of times.
Pandas Series: repeat() function The repeat() function is used to repeat elements of a Series. Returns a new Series where each element of the current Series is repeated consecutively a given number of times. The number of repetitions for each element. This should be a non-negative integer.
Pandas str. repeat() method is used to repeat string values in the same position of passed series itself. An array can also be passed in case to define the number of times each element should be repeated in series.
Use concat:
In [57]:
s = pd.Series(arange(10))
s
Out[57]:
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
dtype: int32
In [59]:
pd.concat([s] * 10, axis=1)
Out[59]:
0 1 2 3 4 5 6 7 8 9
0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3 3 3
4 4 4 4 4 4 4 4 4 4 4
5 5 5 5 5 5 5 5 5 5 5
6 6 6 6 6 6 6 6 6 6 6
7 7 7 7 7 7 7 7 7 7 7
8 8 8 8 8 8 8 8 8 8 8
9 9 9 9 9 9 9 9 9 9 9
If you want to append as rows then remove the axis=1 param.
124
I don't know of any pure numpy solution for what you want but you can use list comprehension and zip to transpose.
df = pd.DataFrame(zip(*[s for i in range(10)]))
Without the comprehension...
df = pd.DataFrame(zip(*[s]*10))
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