How to Stack Only Some Columns in a Data Frame

Pandas, stack some columns, unstack some others

Perhaps you're looking for stack instead of melt:

(df.set_index(['GEOG_CODE', 'COUNTRY', 'Group1'])
   .stack()
   .unstack(-2)
   .ffill(axis=1)
   .bfill(axis=1, downcast='infer')
   .add_prefix('low_')
   .reset_index()
   .rename({'level_2': 'year'}, axis=1))

Group1 GEOG_CODE  COUNTRY  year  low_A  low_B
0            123  England  2016      0      0
1            123  England  2017      0      0
2            123  England  2018      0      0
3            234  England  2016      1      1
4            234  England  2017      1      1
5            234  England  2018      1      1
6            567  England  2016      2      2
7            567  England  2017      2      2
8            567  England  2018      2      2
9            901  England  2016      3      3
10           901  England  2017      3      3
11           901  England  2018      3      3

How do I stack only some columns in a data frame?

Using reshape2 package

dat <- read.table(text = 'A  B  C  V1  V2  V3
1  1  1  x   y   z
1  1  2  a   b   c',header= T)
library(reshape2)
melt(dat,id.vars = c('A','B','C'))
 A B C variable value
1 1 1 1       V1     x
2 1 1 2       V1     a
3 1 1 1       V2     y
4 1 1 2       V2     b
5 1 1 1       V3     z
6 1 1 2       V3     c

How to stack only selected columns in pandas barh plot

Use position parameter to draw 2 columns on the same index:

fig, ax = plt.subplots()
d[['DC', 'DC - IDC']].plot.barh(width=0.4, position=0, stacked=True, ax=ax)
d[['No Address']].plot.barh(width=0.4, position=1, stacked=True, ax=ax, color='red')
plt.show()

Stack some columns of dataframe

You are looking to pd.wide_to_long:

out = pd.wide_to_long(df.rename(columns={'price': 'price_mod'}),
                      stubnames=['package', 'price'],
                      i=['product_id', 'product_name'],
                      j='dummy', sep='_', suffix=r'\w+') \
        .droplevel(level='dummy').reset_index()

Output:

>>> out
   product_id product_name package   price
0     2017210       potato     1KG  3.3900
1     2017210       potato     50g  0.1695
2     2017210       potato    100g  0.3390
3     2017081        onion     1KG  3.5900
4     2017081        onion     50g  0.1795
5     2017081        onion    100g  0.3590
6     2017217        lemon     1KG  2.7900
7     2017217        lemon     50g  0.1395
8     2017217        lemon    100g  0.2790

How to stack only some of pandas DataFrame series on bar chart

Try to plot twice with option stacked and position:

fig, ax = plt.subplots(figsize=(10,6))

# plot first 3 columns, stacked and shifted left
plotdata.iloc[:,:-1].plot.bar(stacked=True, position=1, width=.4, ax=ax)

# plot last column, shifted right
plotdata.iloc[:,-1:].plot.bar(position=0,color='C4', width=.4, ax=ax)

ax.set_xlim(-0.5, len(plotdata)-0.5)

Output:

Unstack or Pivot Only Some Columns

You can set multiindex by first 3 columns and use unstack with level=0.

Then you can rename columns names - or by levels and labels or by custom names.

#  COL1  COL2  COL3   COL4  year
#0    A     1     2  11032  2016
#1    A     2     2   1960  2017
#2    A     3     4  11400  2018
#3    B     1     2  11355  2019
#4    B     2     2      8  2020
#5    B     3     4      7  2021

DF = DF.set_index(['COL1', 'COL2', 'COL3'])
DF = DF.unstack(0).reset_index()
print DF
#     COL2 COL3   COL4         year
#COL1                A      B     A     B
#0       1    2  11032  11355  2016  2019
#1       2    2   1960      8  2017  2020
#2       3    4  11400      7  2018  2021

levels = DF.columns.levels
labels = DF.columns.labels
DF.columns = levels[0][labels[0]]
print DF
#   COL2  COL3   COL4   COL4  year  year
#0     1     2  11032  11355  2016  2019
#1     2     2   1960      8  2017  2020
#2     3     4  11400      7  2018  2021
DF.columns = ['COL2','COL3','COL4','COL5','COL6','COL7']
print DF
#   COL2  COL3   COL4   COL5  COL6  COL7
#0     1     2  11032  11355  2016  2019
#1     2     2   1960      8  2017  2020
#2     3     4  11400      7  2018  2021

But if columns COL2 and COL3 have different order, you can use little hack by Andy Hayden:

#  COL1  COL2  COL3   COL4  year
#0    A     4     6  11032  2016
#1    A     9     2   1960  2017
#2    A     8     4  11400  2018
#3    B     4     6  11355  2019
#4    B     9     2      8  2020
#5    B     8     4      7  2021
DF = DF.set_index(['COL1', 'COL2', 'COL3'])

index = pd.MultiIndex(levels=[DF.index.get_level_values(1).unique(),
                   DF.index.get_level_values(2).unique()],
                   labels=[[0, 1, 2],
                   [0, 1, 2]])
DF = DF.unstack(0)
DF = DF.reindex(index).reset_index()
print DF
#     level_0 level_1   COL4         year
#COL1                      A      B     A     B
#0          4       6  11032  11355  2016  2019
#1          9       2   1960      8  2017  2020
#2          8       4  11400      7  2018  2021
levels = DF.columns.levels
labels = DF.columns.labels
DF.columns = levels[0][labels[0]]
print DF
#   level_0  level_1   COL4   COL4  year  year
#0        4        6  11032  11355  2016  2019
#1        9        2   1960      8  2017  2020
#2        8        4  11400      7  2018  2021
DF.columns = ['COL2','COL3','COL4','COL5','COL6','COL7']
print DF
#   COL2  COL3   COL4   COL5  COL6  COL7
#0     4     6  11032  11355  2016  2019
#1     9     2   1960      8  2017  2020
#2     8     4  11400      7  2018  2021

How to stack two columns of a pandas dataframe in python

You can create a list of the cols, and call squeeze to anonymise the data so it doesn't try to align on columns, and then call concat on this list, passing ignore_index=True creates a new index, otherwise you'll get the names as index values repeated:

cols = [df[col].squeeze() for col in df]
pd.concat(cols, ignore_index=True)

Pandas dataframe fillna() only some columns in place

You can select your desired columns and do it by assignment:

df[['a', 'b']] = df[['a','b']].fillna(value=0)

The resulting output is as expected:

     a    b    c
0  1.0  4.0  NaN
1  2.0  5.0  NaN
2  3.0  0.0  7.0
3  0.0  6.0  8.0

Stacking a number of columns into one column in python

You're looking for pd.wide_to_long. This should do:

pd.wide_to_long(df, stubnames='destination_', i=['source'], j='number')

The column destination_ will have the info you're looking for.

Example:

import pandas as pd
d = {'source': ['a', 'b'],
 'destination_1': ['contact_1', 'contact_6'],
 'destination_2': ['contact_2', 'contact_7']}
df = pd.DataFrame(d)
pd.wide_to_long(df, stubnames='destination_', i=['source'], j='number')

Output:

              destination_
source number             
a      1         contact_1
b      1         contact_6
a      2         contact_2
b      2         contact_7

Selecting/excluding sets of columns in pandas

You can either Drop the columns you do not need OR Select the ones you need

# Using DataFrame.drop
df.drop(df.columns[[1, 2]], axis=1, inplace=True)

# drop by Name
df1 = df1.drop(['B', 'C'], axis=1)

# Select the ones you want
df1 = df[['a','d']]

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