# Plot multiple time series from single dataframe

I have a dataframe with multiple time series and columns with labels. My goal is to plot all time series in a single plot, where the labels should be used in the legend of the plot. The important point is that the x-data of the time series do not match each other, only their ranges roughly do. See this example:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame([[1, 2, "A", "A"], [2, 3, "A", "A"], [3, 1, "A", "A"], [4, 2, "A", "A"], [1.1, 2.3, "B", "B"], [2.3, 3.1, "B", "B"], [3.2, 1.7, "B", "B"], [4.1, 2.8, "B", "B"], [0.9, 2.5, "A", "B"], [1.8, 3.5, "A", "B"], [2.7, 1.2, "A", "B"], [4.4, 5.2, "A", "B"]], columns = ["x", "y", "Cat1", "Cat2"])


The way I got it to work is by looping over the different category-labels, and then plotting the resulting dataframes onto the same ax object:

list1 = set(list(df["Cat1"]))
for Cat1 in list1:
list2 = set(list(df["Cat2"]))
for Cat2 in list2:
ax = plt.gca()
df_temp = df[(df["Cat1"] == Cat1) & (df["Cat2"] == Cat2)]
df_temp.plot(x = "x", y = "y", label = Cat1 + "; " + Cat2, ax = ax)
plt.show()


The result looks like this: Now my question is: Is there a smarter/quicker/more succint way of achieving the same result? E.g. doing something like

df.plot(x = "x", y = "y", label = ["Cat1", "Cat2"])


Not sure if you want to have this done using just pandas/matplotlib, but this can be done relatively easily using the seaborn plotting library:
import seaborn as sns 