Specify ordering for columns when creating a DataFrame from list of dictionaries

I have a method that creates a list, results, and then appends to it a dict row which has keys (column names in my eventual dataframe) and values (row values in my eventual dataframe). Then I append the row and convert the results collection to a df at the end. For example:

results = []
row = {}
row['Name']="alice"
row['Age']=3
results.append(row)
..reset row, fill in values like above, and append more rows..

df = pd.DataFrame(results)

The issue I have is that the column names are alphbetized so the df looks like this:

df.head()
|Age |  Name  |
|3   | alice  |

Is there a way to specify the column names to be in the order I want (“Name”, “Col”)? In reality I have many more columns so I’d prefer not to have to do:

cols = df.columns
cols = cols[:1] + cols[0:1] 

and manually rearrange it. However, if I do so, does that just move around the column row or also the rows below? So, will “alice” and 3 in the row below also get moved around while moving the column as one would expect?

Answer

Specify a list of your desired ordering to the columns attribute, and DataFrame will reorder the columns when creating the DataFrame.

pd.DataFrame(results, columns=['Name', 'Age'])

    Name  Age
0  alice    3

Or, DataFrame.from_records does this as well.

pd.DataFrame.from_records(results, columns=['Name', 'Age'])

    Name  Age
0  alice    3

If you’re working with multiple columns, you could always choose to do columns=['Name', 'Age', *(row.keys() - {'Name', 'Age'})] assuming you don’t care about the ordering of the remaining columns.

I’ve written about constructing DataFrames from records in this post: Convert list of dictionaries to a pandas DataFrame

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Another idea, fix the columns if the ordering is incorrect.

if df.columns.get_loc('Age') < df.columns.get_loc('Name'):
    df.insert(df.columns.get_loc('Age'), 'Name', df.pop('Name'))

This will insert Name before Age if it is originally inserted after.