Imports and Sample DataFrame

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns  # for sample data
from matplotlib.lines import Line2D  # for legend handle

# DataFrame used for all options
df = sns.load_dataset('diamonds')

   carat      cut color clarity  depth  table  price     x     y     z
0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43
1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31
2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31

With matplotlib

You can pass plt.scatter a c argument, which allows you to select the colors. The following code defines a colors dictionary to map the diamond colors to the plotting colors.

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

colors = {'D':'tab:blue', 'E':'tab:orange', 'F':'tab:green', 'G':'tab:red', 'H':'tab:purple', 'I':'tab:brown', 'J':'tab:pink'}

ax.scatter(df['carat'], df['price'], c=df['color'].map(colors))

# add a legend
handles = [Line2D([0], [0], marker='o', color='w', markerfacecolor=v, label=k, markersize=8) for k, v in colors.items()]
ax.legend(title='color', handles=handles, bbox_to_anchor=(1.05, 1), loc='upper left')

plt.show()

df['color'].map(colors) effectively maps the colors from "diamond" to "plotting".

(Forgive me for not putting another example image up, I think 2 is enough :P)

With seaborn

You can use seaborn which is a wrapper around matplotlib that makes it look prettier by default (rather opinion-based, I know :P) but also adds some plotting functions.

For this you could use seaborn.lmplot with fit_reg=False (which prevents it from automatically doing some regression).

• sns.scatterplot(x='carat', y='price', data=df, hue='color', ec=None) also does the same thing.

Selecting hue='color' tells seaborn to split and plot the data based on the unique values in the 'color' column.

sns.lmplot(x='carat', y='price', data=df, hue='color', fit_reg=False)

With pandas.DataFrame.groupby & pandas.DataFrame.plot

If you don't want to use seaborn, use pandas.groupby to get the colors alone, and then plot them using just matplotlib, but you'll have to manually assign colors as you go, I've added an example below:

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

grouped = df.groupby('color')
for key, group in grouped:
    group.plot(ax=ax, kind='scatter', x='carat', y='price', label=key, color=colors[key])
plt.show()

This code assumes the same DataFrame as above, and then groups it based on color. It then iterates over these groups, plotting for each one. To select a color, I've created a colors dictionary, which can map the diamond color (for instance D) to a real color (for instance tab:blue).

You can use scatter for this, but that requires having numerical values for your key1, and you won't have a legend, as you noticed.

It's better to just use plot for discrete categories like this. For example:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(1974)

# Generate Data
num = 20
x, y = np.random.random((2, num))
labels = np.random.choice(['a', 'b', 'c'], num)
df = pd.DataFrame(dict(x=x, y=y, label=labels))

groups = df.groupby('label')

# Plot
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for name, group in groups:
    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name)
ax.legend()

plt.show()

If you'd like things to look like the default pandas style, then just update the rcParams with the pandas stylesheet and use its color generator. (I'm also tweaking the legend slightly):

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(1974)

# Generate Data
num = 20
x, y = np.random.random((2, num))
labels = np.random.choice(['a', 'b', 'c'], num)
df = pd.DataFrame(dict(x=x, y=y, label=labels))

groups = df.groupby('label')

# Plot
plt.rcParams.update(pd.tools.plotting.mpl_stylesheet)
colors = pd.tools.plotting._get_standard_colors(len(groups), color_type='random')

fig, ax = plt.subplots()
ax.set_color_cycle(colors)
ax.margins(0.05)
for name, group in groups:
    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name)
ax.legend(numpoints=1, loc='upper left')

plt.show()