Reference: https://www.mikulskibartosz.name/how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/

Given a table containing a column of lists, like:

Like the ‘unwind’ function in mongodb, to turn it into:

Here’s who we do it:

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tags_df['tags'] = tags_df.tags.apply(lambda x: x[1:-1].split(','))
clean_tag_df = tags_df.tags.apply(pd.Series).merge(tags_df, right_index = True, left_index = True) \
    .drop(["tags"], axis = 1) \
    .melt(id_vars = ['recipe_id'], value_name = "tags") \
    .drop("variable", axis = 1).dropna()

Breaking it down, we have the following to turn the tags from a string into a list of strings.

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tags_df['tags'] = tags_df.tags.apply(lambda x: x[1:-1].split(','))

Next step, turning a list of tags into multiple columns:

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tags_df.tags.apply(pd.Series)

It turns the chart like:

Then, we join tags with the rest of the list:

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prev_df.merge(tags_df, right_index = True, left_index = True)

Then, we drop the duplicated “tags” column and unwind different columns of the tags into different rows:

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prev_df.drop(["tags"], axis = 1).melt(id_vars = ['recipe_id'], value_name = "tags")

Lastly, we remove the “variable” column, which we might not need:

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prev_df.drop("variable", axis = 1).dropna()

A simple cheat-sheet for confusion matrix.

To change a row value based on more than one conditions, use the following:

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df['A'] = np.where(((df['B'] == 'some_value') & (df['C'] == 'some_other_value')), true_value, false_value)

Another equally efficient approach is using loc:
Note that this method does not give a default value when the condition is not met. So, the same code might be required to run twice or using .fillna() method.

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df.loc[(df.B == 'some_value') | (df.C == 'some_other_value'), 'A'] = true_value

ps: for ‘and’ or ‘or’ operation, use symbol ‘&’ or ‘|’.