CODEWITHSUNDEEP
pythonpandasdataframe
hurrikale
hurrikale

Reputation: 345

Pandas: create dataframe without auto ordering column names alphabetically

I am creating an initial pandas dataframe to store results generated from other codes: e.g.

result = pd.DataFrame({'date': datelist, 'total': [0]*len(datelist), 
                       'TT': [0]*len(datelist)})

with datelist a predefined list. Then other codes will output some number for total and TT for each date, which I will store in the result dataframe.

So I want the first column to be date, second total and third TT. However, pandas will automatically reorder it alphabetically to TT, date, total at creation. While I can manually reorder this again afterwards, I wonder if there is an easier way to achieve this in one step.

I figured I can also do

result = pd.DataFrame(np.transpose([datelist, [0]*l, [0]*l]),
                      columns = ['date', 'total', 'TT'])

but it somehow also looks tedious. Any other suggestions?

Upvotes: 15

Views: 14991

Answers (3)

kadee
kadee

Reputation: 8884

Use pandas >= 0.23 in combination with Python >= 3.6.

result = pd.DataFrame({'date': datelist, 'total': [0]*len(datelist), 'TT': [0]*len(datelist)})

retains the dict's insertion order when creating a DataFrame (or Series) from a dict when using pandas v0.23.0 in combination with Python3.6.

See https://pandas.pydata.org/pandas-docs/version/0.23.0/whatsnew.html#whatsnew-0230-api-breaking-dict-insertion-order.

Upvotes: 2

wonce
wonce

Reputation: 1921

You can pass the (correctly ordered) list of column as parameter to the constructor or use an OrderedDict:

# option 1:
result = pd.DataFrame({'date': datelist, 'total': [0]*len(datelist), 
                   'TT': [0]*len(datelist)}, columns=['date', 'total', 'TT'])

# option 2:
od = collections.OrderedDict()
od['date'] = datelist
od['total'] = [0]*len(datelist)
od['TT'] = [0]*len(datelist)
result = pd.DataFrame(od)

Upvotes: 15

python必须死
python必须死

Reputation: 1089

result = pd.DataFrame({'date': [23,24], 'total': 0,
                       'TT': 0},columns=['date','total','TT'])

Upvotes: 3

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