在 seaborn 或 matplotlib 中绘制分类散点图

Question

I have the following dataframe

   it, A   B   C   D
0  10, aa  mn  cd  kk
1  100, ab  cd  wc  ll
2  1000, wc  cd  mn  sf
3  10000, ll  ll  kk  mn
4  100000, wc  kk  mn  cd
5  1000000, aa  ll  we  sf
6  10000000, ss  aa  ss  kk

created as

options = ["ab", "cd", "bb", "aa", "we", "ss", "kk", "mn", "re", "wc", "ll", "sf"]
df = pd.DataFrame(columns=["A", "B", "C", "D"])
for i, it in enumerate([1,2,3,4,5,6,7]):
    row = [10**i, random.sample(options, 1)[0], random.sample(options, 1)[0], 
           random.sample(options, 1)[0], random.sample(options, 1)[0]]
    df.loc[i] = row

The goal is to create a scatterplot where y axis are unique values from a dataframe in sorted order eg options and a-axis corresponds to column it . Now depending on whether data belongs to column A, B, C, or D I want to color scatter-dots differently and specify a legend. So I know what class a dot comes from.

How do I do it in seaborn or matplotlib?

The way I am doing it in matplotlib is

iters = list(range(df.shape[0]))
x, y = sort(iters, df["A"])
plt.scatter(x, y, color="red")
x, y = sort(iters, df["B"])
plt.scatter(x, y, color="blue")
...

but that does not sort the entire y-axis, only labels that belong to separate columns.

Answer 1

Let's try stack the data, convert to categorical with given order, sort and plot:

s = df.stack() 

s = pd.Series(pd.Categorical(s, categories=options, ordered=True),
              index=s.index)

sns.scatterplot(data=s.sort_values().reset_index(name='value'),
                x='level_0', y='value', hue='level_1'
               )

Output:

---

Update : if you have a column xvalue and only care for some columns ['A','B','C','D'] , use melt instead of stack :

s = df.melt(id_vars='xvalue', 
            value_vars=['A','B','C','D'],
            value_name='value',
            var_name='column')
s['value'] = pd.Categorical(s['value'], categories=options, ordered=True)

sns.scatterplot(data=s.sort_values('value'),
                x='xvalue', y='value', hue='column'
               )