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// 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'
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# 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)
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>>> s.sort_values(ascending=False, inplace=True)
>>> s
3 10.0
4 5.0
2 3.0
1 1.0
0 NaN
dtype: float64
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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'])
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df.sort_values(by='col1', ascending=False)
col1 col2 col3 col4
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
3 NaN 8 4 D
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s.sort_values(ascending=True)
1 1.0
2 3.0
4 5.0
3 10.0
0 NaN
dtype: float64
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# Sorting Pandas Dataframe in Descending Order
# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
age_list = [['Afghanistan', 1952, 8425333, 'Asia'],
['Australia', 1957, 9712569, 'Oceania'],
['Brazil', 1962, 76039390, 'Americas'],
['China', 1957, 637408000, 'Asia'],
['France', 1957, 44310863, 'Europe'],
['India', 1952, 3.72e+08, 'Asia'],
['United States', 1957, 171984000, 'Americas']]
# creating a pandas dataframe
df = pd.DataFrame(age_list, columns=['Country', 'Year',
'Population', 'Continent'])
# Sorting by column "Population"
df.sort_values(by=['Population'], ascending=False)
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df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3 col4
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
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>>> s.sort_values(ascending=True)
1 1.0
2 3.0
4 5.0
3 10.0
0 NaN
dtype: float64