How To Sort Pandas DataFrame ?
Table Of Contents:
- Syntax ‘sort_values( )’ Method In Pandas.
- Examples ‘sort_values( )’ Method.
(1) Syntax:
DataFrame.sort_values(by, *, axis=0, ascending=True, inplace=False, kind='quicksort',
na_position='last', ignore_index=False, key=None)Description:
- Sort by the values along either axis.
Parameters:
- by: str or list of str –
Name or list of names to sort by.
if axis is 0 or ‘index’ then by may contain index levels and/or column labels.
if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.
- axis: {0 or ‘index’, 1 or ‘columns’}, default 0 –
- Axis to be sorted.
- ascending: bool or list of bool, default True –
- Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.
- inplace: bool, default False –
- If True, perform operation in-place.
- kind: {‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’ –
- Choice of sorting algorithm. See also
numpy.sort()for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
- Choice of sorting algorithm. See also
Returns:
- DataFrame or None –
- DataFrame with sorted values or None if
inplace=True.
- DataFrame with sorted values or None if
(2) Examples Of sort_values( ) Method:
Example-1:
df = pd.DataFrame({
'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
'col2': [2, 1, 9, 8, 7, 4],
'col3': [0, 1, 9, 4, 2, 3],
'col4': ['a', 'B', 'c', 'D', 'e', 'F']
})
dfOutput:
# Sort by col1
df.sort_values(by=['col1'])Output:
Note:
- If you are sorting using multiple columns then, it will sort using the first column and then it will sort the repeated values in our example it is ‘A’ using the 2nd column.
# Sort by multiple columns
df.sort_values(by=['col1', 'col2'])Output:
# Sort Descending
df.sort_values(by='col1', ascending=False)Output:
# Putting NAs first
df.sort_values(by='col1', ascending=False, na_position='first')Output:
# Sorting with a key function
df.sort_values(by='col4', key=lambda col: col.str.lower())Output:
