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I realize Dataframe takes a map of {'series_name':Series(data, index)}. However, it automatically sorts that map even if the map is an OrderedDict().
Is there a simple way to pass a list of Series(data, index, name=name) such that the order is preserved and the column names are the series.name? Is there an easy way if all the indices are the same for all the series?
I normally do this by just passing a numpy column_stack of series.values and specifying the column names. However, this is ugly and in this particular case the data is strings not floats.
847
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.
Pandas series can be converted to a list using tolist() or type casting method. There can be situations when you want to perform operations on a list instead of a pandas object. In such cases, you can store the DataFrame columns in a list and perform the required operations.
Appending a multiple rows - Appending a list of Dictionaries to a DataFrame. You can also pass a list of Series or a list of Dictionaries to append multiple rows.
You could use pandas.concat:
import pandas as pd from pandas.util.testing import rands data = [pd.Series([rands(4) for j in range(6)], index=pd.date_range('1/1/2000', periods=6), name='col'+str(i)) for i in range(4)] df = pd.concat(data, axis=1, keys=[s.name for s in data]) print(df) yields
col0 col1 col2 col3 2000-01-01 GqcN Lwlj Km7b XfaA 2000-01-02 lhNC nlSm jCYu XLVb 2000-01-03 sSRz PFby C1o5 0BJe 2000-01-04 khZb Ny9p crUY LNmc 2000-01-05 hmLp 4rVp xF2P OmD9 2000-01-06 giah psQb T5RJ oLSh If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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