I'm new to pandas and working with tabular data in a programming environment. I have sorted a dataframe by a specific column but the answer that panda spits out is not exactly correct.
Here is the code I have used:
league_dataframe.sort_values('overall_league_position')
The result that the sort method yields values in column 'overall league position' are not sorted in ascending or order which is the default for the method.
What am I doing wrong? Thanks for your patience!
For whatever reason, you seem to be working with a column of strings, and sort_values
is returning you a lexsorted result.
Here's an example.
df = pd.DataFrame({"Col": ['1', '2', '3', '10', '20', '19']})
df
Col
0 1
1 2
2 3
3 10
4 20
5 19
df.sort_values('Col')
Col
0 1
3 10
5 19
1 2
4 20
2 3
The remedy is to convert it to numeric, either using .astype
or pd.to_numeric
.
df.Col = df.Col.astype(float)
Or,
df.Col = pd.to_numeric(df.Col, errors='coerce')
df.sort_values('Col')
Col
0 1
1 2
2 3
3 10
5 19
4 20
The only difference b/w astype
and pd.to_numeric
is that the latter is more robust at handling non-numeric strings (they're coerced to NaN
), and will attempt to preserve integers if a coercion to float is not necessary (as is seen in this case).
Using sort_naturally function instead of sort_values works well for numbers. Below is the sytax:
league_dataframe.sort_naturally('overall_league_position')
Natural sorting is distinct from the default lexicographical sorting provided by pandas
.
For example, given the following list of items:
["A1", "A11", "A3", "A2", "A10"]
lexicographical sorting would give us:
["A1", "A10", "A11", "A2", "A3"]
By contrast, "natural" sorting would give us:
["A1", "A2", "A3", "A10", "A11"]
This function thus provides "natural" sorting on a single column of a data frame. For further information type the following command in the console or a jupyter notebook code cell ?league_dataframe.sort_naturally
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