Sort df by column

Sort dataframe by column

df.sort_values(by='col1', ascending=False)

Sort a dataframe by a column valuepython

>>> df.sort_values(by=['col1'])
    col1 col2 col3
0   A    2    0
1   A    1    1
2   B    9    9
5   C    4    3
4   D    7    2
3   NaN  8    4

Source: pandas.pydata.org

Sorting by column in pandas

# Python, Pandas
# Sorting dataframe df on the values of a column col1

# Return sorted array without modifying the original one
df.sort_values(by=["col1"]) 

# Sort the original array permanently
df.sort_values(by=["col1"], inplace = True)

How to sort in pandas

// Single sort 
>>> df.sort_values(by=['col1'],ascending=False)
// ascending => [False(reverse order) & True(default)]
// Multiple Sort
>>> df.sort_values(by=['col1','col2'],ascending=[True,False])
// with apply() 
>>> df[['col1','col2']].apply(sorted,axis=1)
// axis = [1 & 0], 1 = 'columns', 0 = 'index'

Sort by dataframe

DataFrame.sort_values(self, by, axis=0, ascending=True,
                      inplace=False, kind='quicksort',
                      na_position='last',
                      ignore_index=False)

# Example
df.sort_values(by=['ColToSortBy'])

Sort df by column

df.rename(columns={1:'month'},inplace=True)
df['month'] = pd.Categorical(df['month'],categories=['December','November','October','September','August','July','June','May','April','March','February','January'],ordered=True)
df = df.sort_values('month',ascending=False)

Source: stackoverflow.com

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