pandas.melt()

pandas.melt

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name=’value’, col_level=None)

Parameters:

• frame : DataFrame
• id_vars : tuple, list, or ndarray, optional，Column(s) to use as identifier variables.
• value_vars : tuple, list, or ndarray, optional，Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
• var_name : scalarName to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
• value_name : scalar, default ‘value’Name to use for the ‘value’ column.
• col_level : int or string, optional, If columns are a MultiIndex then use this level to melt.

简单讲：将一个DataFrame根据id_vars列，把value_vars列的值平铺展开，不设置值时，默认为所有列。

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
warnings.filterwarnings('ignore')

举例

df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
                    'B': {0: 1, 1: 3, 2: 5},
                    'C': {0: 2, 1: 4, 2: 6}})
df

	A	B	C
0	a	1	2
1	b	3	4
2	c	5	6

pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])

	A	variable	value
0	a	B	1
1	b	B	3
2	c	B	5
3	a	C	2
4	b	C	4
5	c	C	6

seaborn iris数据集

df= sns.load_dataset('iris')
df.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

df=pd.melt(df,'species', var_name="measurement")
df.head()

	species	measurement	value
0	setosa	sepal_length	5.1
1	setosa	sepal_length	4.9
2	setosa	sepal_length	4.7
3	setosa	sepal_length	4.6
4	setosa	sepal_length	5.0

sns.stripplot()

sns.set(style="whitegrid")
iris = sns.load_dataset("iris")

# "Melt" the dataset to "long-form" or "tidy" representation
iris = pd.melt(iris, "species", var_name="measurement")

# Initialize the figure
f, ax = plt.subplots()
sns.despine(bottom=True, left=True)

# Show each observation with a scatterplot
sns.stripplot(x="value", y="measurement", hue="species",
              data=iris, dodge=True, alpha=.25, zorder=1)

# Show the conditional means
sns.pointplot(x="value", y="measurement", hue="species",
              data=iris, dodge=.532, join=False, palette="dark",
              markers="d", scale=.75, ci=None)

# Improve the legend 
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[3:], labels[3:], title="species",
          handletextpad=0, columnspacing=1,
          loc="lower right", ncol=3, frameon=True)