Difference between df[x], df[[x]], df['x'] , df[['x']] and df.x

Struggling to understand the difference between the 5 examples in the title. Are some use cases for series vs. data frames? When should one be used over the other? Which are equivalent?

1 Answer

1. df[x] — index a column using variable x. Returns pd.Series
2. df[[x]] — index/slice a single-column DataFrame using variable x. Returns pd.DataFrame
3. df['x'] — index a column named 'x'. Returns pd.Series
4. df[['x']] — index/slice a single-column DataFrame having only one column named 'x'. Returns pd.DataFrame
5. df.x — dot accessor notation, equivalent to df['x'] (there are, however, limitations on what x can be named if dot notation is to be successfully used). Returns pd.Series

With single brackets [...] you may only index a single column out as a Series. With double brackets, [[...]], you may select as many columns as you need, and these columns are returned as part of a new DataFrame.

Setup

df ID x 0 0 0 1 1 15 2 2 0 3 3 0 4 4 0 5 5 15 x = 'ID' 

Examples

df[x] 0 0 1 1 2 2 3 3 4 4 5 5 Name: ID, dtype: int64 type(df[x]) pandas.core.series.Series 

df['x'] 0 0 1 15 2 0 3 0 4 0 5 15 Name: x, dtype: int64 type(df['x']) pandas.core.series.Series 

df[[x]] ID 0 0 1 1 2 2 3 3 4 4 5 5 type(df[[x]]) pandas.core.frame.DataFrame 

df[['x']] x 0 0 1 15 2 0 3 0 4 0 5 15 type(df[['x']]) pandas.core.frame.DataFrame 

df.x 0 0 1 15 2 0 3 0 4 0 5 15 Name: x, dtype: int64 type(df.x) pandas.core.series.Series 

Further reading
Indexing and Selecting Data