如何将单元格中的字典列表拆分为 dataframe 中的多行？

Question

inp = [{'Name':'Jack', 'Twitter':'twitter.com/Jack', 'Stats': [{'Year': 2018, 'Followers': '5000'}, {'Year':2019, 'Followers': '6000'}]}, {'Name':'Bill','Twitter':'twitter.com/Twitter'}]
df = pd.DataFrame(inp)

Say I have a DataFrame like so:

| Name | Twitter             | Stats                                                                     |
|------|---------------------|---------------------------------------------------------------------------|
| Jack | twitter.com/Jack    | [{'Year': 2018, 'Followers': '5000'}, {'Year': 2019, 'Followers': '6000'}] |
| Bill | twitter.com/Twitter |                                                                           |

How can I split the stats into multiple rows and subsequently split the dictionary into multiple columns? Also, if there is a NaN, just ignore the row.

Desired output:

| Name | Twitter             | Year | Followers |
|------|---------------------|------|-----------|
| Jack | twitter.com/Jack    | 2018 | 5000      |
| Jack | twitter.com/Jack    | 2019 | 6000      |
| Bill | twitter.com/Twitter |      |           |

Answer 1

Here is my solution for this:

clms = ['Name', 'Twitter', 'Year', 'Followers']
new_df = pd.DataFrame(columns=clms)
for idx, row in df.iterrows():
    try:
        for elt in row.Stats:
            new_series = pd.Series([row.Name, row.Twitter, elt['Year'], elt['Followers']], index=clms)
            new_df = new_df.append(new_series, ignore_index=True)
    except TypeError:
        new_series = pd.Series([row.Name, row.Twitter, np.NaN, np.NaN], index=clms)
        new_df = new_df.append(new_series, ignore_index=True)

So basically, this might not be super performant, but it creates a new dataframe with the corresponding split values, or NaN if the is no value in the dataframe