get_melt function# 

[1]:

from plotnine.data import mtcars
from ggcorrplot import get_melt
mtcars = mtcars.set_index("name") # set name as index
print(mtcars)

                      mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \
name
Mazda RX4            21.0    6  160.0  110  3.90  2.620  16.46   0   1     4
Mazda RX4 Wag        21.0    6  160.0  110  3.90  2.875  17.02   0   1     4
Datsun 710           22.8    4  108.0   93  3.85  2.320  18.61   1   1     4
Hornet 4 Drive       21.4    6  258.0  110  3.08  3.215  19.44   1   0     3
Hornet Sportabout    18.7    8  360.0  175  3.15  3.440  17.02   0   0     3
Valiant              18.1    6  225.0  105  2.76  3.460  20.22   1   0     3
Duster 360           14.3    8  360.0  245  3.21  3.570  15.84   0   0     3
Merc 240D            24.4    4  146.7   62  3.69  3.190  20.00   1   0     4
Merc 230             22.8    4  140.8   95  3.92  3.150  22.90   1   0     4
Merc 280             19.2    6  167.6  123  3.92  3.440  18.30   1   0     4
Merc 280C            17.8    6  167.6  123  3.92  3.440  18.90   1   0     4
Merc 450SE           16.4    8  275.8  180  3.07  4.070  17.40   0   0     3
Merc 450SL           17.3    8  275.8  180  3.07  3.730  17.60   0   0     3
Merc 450SLC          15.2    8  275.8  180  3.07  3.780  18.00   0   0     3
Cadillac Fleetwood   10.4    8  472.0  205  2.93  5.250  17.98   0   0     3
Lincoln Continental  10.4    8  460.0  215  3.00  5.424  17.82   0   0     3
Chrysler Imperial    14.7    8  440.0  230  3.23  5.345  17.42   0   0     3
Fiat 128             32.4    4   78.7   66  4.08  2.200  19.47   1   1     4
Honda Civic          30.4    4   75.7   52  4.93  1.615  18.52   1   1     4
Toyota Corolla       33.9    4   71.1   65  4.22  1.835  19.90   1   1     4
Toyota Corona        21.5    4  120.1   97  3.70  2.465  20.01   1   0     3
Dodge Challenger     15.5    8  318.0  150  2.76  3.520  16.87   0   0     3
AMC Javelin          15.2    8  304.0  150  3.15  3.435  17.30   0   0     3
Camaro Z28           13.3    8  350.0  245  3.73  3.840  15.41   0   0     3
Pontiac Firebird     19.2    8  400.0  175  3.08  3.845  17.05   0   0     3
Fiat X1-9            27.3    4   79.0   66  4.08  1.935  18.90   1   1     4
Porsche 914-2        26.0    4  120.3   91  4.43  2.140  16.70   0   1     5
Lotus Europa         30.4    4   95.1  113  3.77  1.513  16.90   1   1     5
Ford Pantera L       15.8    8  351.0  264  4.22  3.170  14.50   0   1     5
Ferrari Dino         19.7    6  145.0  175  3.62  2.770  15.50   0   1     5
Maserati Bora        15.0    8  301.0  335  3.54  3.570  14.60   0   1     5
Volvo 142E           21.4    4  121.0  109  4.11  2.780  18.60   1   1     4

                     carb
name
Mazda RX4               4
Mazda RX4 Wag           4
Datsun 710              1
Hornet 4 Drive          1
Hornet Sportabout       2
Valiant                 1
Duster 360              4
Merc 240D               2
Merc 230                2
Merc 280                4
Merc 280C               4
Merc 450SE              3
Merc 450SL              3
Merc 450SLC             3
Cadillac Fleetwood      4
Lincoln Continental     4
Chrysler Imperial       4
Fiat 128                1
Honda Civic             2
Toyota Corolla          1
Toyota Corona           1
Dodge Challenger        2
AMC Javelin             2
Camaro Z28              4
Pontiac Firebird        2
Fiat X1-9               1
Porsche 914-2           2
Lotus Europa            2
Ford Pantera L          4
Ferrari Dino            6
Maserati Bora           8
Volvo 142E              2

[2]:

#from wide to long
from ggcorrplot import get_melt
mtcars_long = get_melt(mtcars).rename(columns={"Var1" : "name", "Var2" : "Variable"})
print(mtcars_long)

           name Variable  value
0     Mazda RX4      mpg   21.0
1     Mazda RX4      cyl    6.0
2     Mazda RX4     disp  160.0
3     Mazda RX4       hp  110.0
4     Mazda RX4     drat    3.9
..          ...      ...    ...
347  Volvo 142E     qsec   18.6
348  Volvo 142E       vs    1.0
349  Volvo 142E       am    1.0
350  Volvo 142E     gear    4.0
351  Volvo 142E     carb    2.0

[352 rows x 3 columns]

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