Converting NumPy Date in Pandas DataFrame without using Pd.DataFrame-CodePudding

I have a question in an assessment where i have to convert NumPy data into Pandas dataframe type. It also should use the data's dtype names as column headers.

I cannot use the pd.DataFrame() function for this task and there has been a clue given where I am supposed to still use pandas methods.

This is the code i have so far -

    def convert_to_df(data):
        "converting numpy array into dataframe"
        far = data.tolist()
        return pd.Series(far).to_frame()

which does convert it to a DataFrame ,giving this with the test:

                                                   0
0  (2020-02-29 13:32:59, 1.23375E 18, 0.67, 0.293...
1  (2020-02-27 00:20:58, 1.23282E 18, 0.442, 0.38...
2  (2020-02-10 18:54:50, 1.22694E 18, 0.577, 0.42...
3  (2020-02-29 05:23:06, 1.23362E 18, 0.514, 0.41...
4  (2020-02-26 03:20:55, 1.23251E 18, 0.426, 0.37...

im just confused on how to then get the headers in order. My output when i run test code is meant to look like this.

            created_at  ...     emotion_category
0  2020-02-29 13:32:59  ...                  joy
1  2020-02-27 00:20:58  ...                 fear
2  2020-02-10 18:54:50  ...                  joy
3  2020-02-29 05:23:06  ...  no specific emotion
4  2020-02-26 03:20:55  ...                 fear

[5 rows x 9 columns]

I have attached a screenshot of the question so you can see the test codes and the wording. Hope someone can help !

the data im using looks like this

CodePudding user response：

You can use pandas series instead, what I would do is convert each column in the numpy array to a Series, for example, I have the following numpy array:

data = np.array([[1, 2, 3], [4, 5, 6]])

I will use a for loop to create series for each column:

series = []
for i in range(data.shape[1]):
    series.append(pd.Series(data[:,i], name="Serie_"   str(i)))

Finally, concatenate these series to one dataframe:

pd.concat([series[i] for i in range(data.shape[1])], axis=1)

The result:

   Serie_0  Serie_1  Serie_2
0        1        2        3
1        4        5        6

I hope this helps.