Category: Python

Sometimes, we want to create a row for each list element from a Python Pandas column of lists.

In this article, we’ll look at how to create a row for each list element from a Python Pandas column of lists.

How to create a row for each list element from a Python Pandas column of lists?

To create a row for each list element from a Python Pandas column of lists, we call explode with the column with the lists to create the rows from.

For instance, we write

df = pd.DataFrame({
    'var1': [['a', 'b', 'c'], ['d', 'e',], [], np.nan], 
    'var2': [1, 2, 3, 4]
})
df.explode('var1')

to call explode with 'var' to unpack the lists in the var1 column and create column values from them.

We can reset the indexes by calling explode with `reset_index

df.explode('var1').reset_index(drop=True)

so that the indexes are updated after creating the next values.

Conclusion

To create a row for each list element from a Python Pandas column of lists, we call explode with the column with the lists to create the rows from.

Sometimes, we want to convert a Python Pandas DataFrame to a dictionary.

In this article, we’ll look at how to convert a Python Pandas DataFrame to a dictionary.

How to convert a Python Pandas DataFrame to a dictionary?

To convert a Python Pandas DataFrame to a dictionary, we call to_dict.

For instance, we write

df = pd.DataFrame({'a': ['red', 'yellow', 'blue'], 'b': [0.5, 0.25, 0.125]})
df.to_dict('dict')

to create the data frame df.

Then we call to_dict with 'dict' to return the dictionary from the df data frame.

Conclusion

To convert a Python Pandas DataFrame to a dictionary, we call to_dict.

Sometimes, we want to pretty-print an entire Python Pandas Series or DataFrame.

In this article, we’ll look at how to pretty-print an entire Python Pandas Series or DataFrame.

How to pretty-print an entire Python Pandas Series or DataFrame?

To pretty-print an entire Python Pandas Series or DataFrame, we can use the to_string method.

For instance, we write

print(df.to_string())

to call to_string on the df dataframe to return it as a pretty printed string.

Then we call print with it to print it onto the screen.

Conclusion

To pretty-print an entire Python Pandas Series or DataFrame, we can use the to_string method.

Sometimes, we want to groupby value counts on the dataframe with Python Pandas.

In this article, we’ll look at how to groupby value counts on the dataframe with Python Pandas.

How to groupby value counts on the dataframe with Python Pandas?

To groupby value counts on the dataframe with Python Pandas, we call groupby with size.

For instance, we write

df.groupby(['id', 'group', 'term']).size().unstack(fill_value=0)

to call groupby with a list of columns we want to group by.

And then we call size to get the counts of each group.

Conclusion

To groupby value counts on the dataframe with Python Pandas, we call groupby with size.

To keep array type as integer while having a NaN value with Python Pandas, to call dropna to drop toe NaN values.

And then we call apply to convert the remaining number values to ints.

For instance, we write

df.col = df.col.dropna().apply(lambda x: str(int(x)) )

to call dropna to drop the NaN values from the col column.

Then we call apply with a lambda function that calls int with x to convert x to an int.

And then we call str to convert the int to a string.