Pandas: Create Named Columns In Dataframe From Dict
Solution 1:
You can iterate through the items:
In [11]: pd.DataFrame(list(my_dict.iteritems()),
columns=['business_id','business_code'])
Out[11]:
business_id business_code
0 id2 val2
1 id3 val3
2 id1 val1
Solution 2:
To get the same functionality as the documentation and avoid using code workarounds, make sure you're using the most recent version of Pandas. I recently encountered the same error when running a line of code from the Pandas tutorial:
pd.DataFrame.from_dict(dict([('A', [1, 2, 3]), ('B', [4, 5, 6])]),orient='index', columns=['one', 'two', 'three'])
I checked the version of Pandas and found I was running version 22, when version 23 is available.
import pandas as pd
pd.__version__Out[600]: '0.22.0'
I upgraded using pip:
c:\pip install --upgrade pandas
I confirmed my version updated to 23, and the same from_dict() code worked without error. No code modifications required.
Solution 3:
From version 0.23.0, you can specify a columns
parameter in from_dict
:
my_dict = {id1: val1, id2: val2, id3: val3, ...}
prepared_dict = {i: x for i, x in enumerate(my_dict.items())}
df = pd.DataFrame.from_dict(prepared_dict, orient='index', columns=['business_id', 'business_code'])
Note: I also answered in kind on this similar question.
Solution 4:
Do this:
create the dataframe
df = pd.DataFrame(data_as_2d_ndarray)
create a sorted list of column names from the dictionary - adjust the key karg as need to grab the sorting value from your dict, obvilous the dictionary the data must have consistent shapes
col_names = sorted(list(col_dict.iteritems()),key=lambda x:x[0])
reshape and set the column names
df.columns = zip(*col_names)[1]
Solution 5:
This is with respect to TypeError you faced. As per Pandas documentation, from_dict will take the keyword 'columns' only if the orient = 'index'.
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