PYTHON

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)

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

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'

pandas sort columns by name

df = df.reindex(sorted(df.columns), axis=1)

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'])

dataframe sort by column

sorted = df.sort_values('column-to-sort-on', ascending=False)
#or
df.sort_values('name', inplace=True) 

dataframe, sort by columns

final_df = df.sort_values(by=['2'], ascending=False)

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)

pandas sort

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

pandas sort by columns

# Python, Pandas
# Sorting dataframe

# sort by one column
df.sort_values(by=['col1']) 

# sort by more columns
df.sort_values(by=['col1', 'col2'])

# defaut values
# DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

Sorting Dataframes by Column Python Pandas

# 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)

sort columns dataframe

df = df.reindex(sorted(df.columns), axis=1)

pandas sort dataframe by column

# Basic syntax:
import pandas as pd
df.sort_values(by=['col1'])

# Note, this does not sort in place unless you add inplace=True
# Note, add ascending=False if you want to sort in decreasing order
# Note, to sort by more than one column, add other column names to the
#	list like by=['col1', 'col2']

pandas sort

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

pandas dataframe sort by column

df.sort_values(by=['col1])

python dataframe sort by column name

>>> result = df.sort(['A', 'B'], ascending=[1, 0])

sort dataframe by function

df = pd.DataFrame(['bit_0', 'bit_2', 'bit_5'], columns=['bit'])
df.sort_values('bit' ,key=lambda x: x.str.split("_",expand=True)[1].astype(int))

sort a dataframe

sort_na_first = gapminder.sort_values('lifeExp',na_position='first')