How to plot the dataframes with Permutations?-CodePudding

I am not sure if my title is a precise title or not. If not, feel free to edit it!

Hi! I have three dataframes and want to plot it to scatter plots. But currently, I just write in three different plots and don't know how to write into one. To be more specific, according to the below codes, I want to do like this. x[i], y[i], y[i], z[i]and x[i], z[i]are three choices, so i need to adjust the variable manually. Also, the kind is a list, contain three strings (kind = ["food", "meat", "vege"]) , and if kind1 and kind2 equal 0 and 1, it is for x and y; if kinds equal 0 and 2 respectively, it is for x and z; if kind1 and kind2 are 1 and 2, it's for y and z.

Here are some dataframes you can try.

food = {'col1': [1, 2], 'col2': [3, 4]}
food_df = pd.DataFrame(data=food)

meat = {'col1': [3, 6], 'col2': [1, 2]}
meat_df = pd.DataFrame(data=meat)

vege = {'col1': [5, 9], 'col2': [0, 3]}
vege_df = pd.DataFrame(data=vege)

Here is my current codes

fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(111)

kind = ["fooood", "meats", "vegea"]
features = ["Attack", "Volume"]
kind1 = 1
kind2 = 2
col = ["dd", "aa"]
def plot_scatter(feature):
    ind = features.index(feature)
    x = food_df.iloc[ind]
    y = meat_df.iloc[ind]
    z = vege_df.iloc[ind]
    for i, c in enumerate(np.unique(col)):
        plt.scatter(y[i], z[i], c = "b")
    for i, txt in enumerate(col):
        ax.annotate(txt, (y[i], z[i]), xytext=(y[i] 0.0001, z[i] 0.0001), size = 12)

    plt.xlabel(kind[kind1], size = 15)
    plt.ylabel(kind[kind2], size = 15)
    plt.title(f"{feature}")

plot_scatter("Attack")
plot_scatter("Volume")

I appreciate it!!!!!!!!

CodePudding user response：

Edit:

After reading the dependency of kind1 and kind2 on the choice of x, y and z, I think this is better suited:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

food = {"col1": [1, 2], "col2": [3, 4]}
food_df = pd.DataFrame(data=food)

meat = {"col1": [3, 6], "col2": [1, 2]}
meat_df = pd.DataFrame(data=meat)

vege = {"col1": [5, 9], "col2": [0, 3]}
vege_df = pd.DataFrame(data=vege)
# Here is my current codes

kind = ["fooood", "meats", "vegea"]
features = ["Attack", "Volume"]
col = ["dd", "aa"]


def plot_scatter(feature):
    ind = features.index(feature)
    x = food_df.iloc[ind]
    y = meat_df.iloc[ind]
    z = vege_df.iloc[ind]
    combinations = ((0, 1, x, y), (0, 2, x, z), (1, 2, y, z))

    fig, axes = plt.subplots(1, 3, figsize=(15, 10))

    for idx, (kind1, kind2, col1, col2) in enumerate(combinations):
        ax = axes[idx]

        for i, c in enumerate(np.unique(col)):
            ax.scatter(col1, col2, c="b")

        for i, txt in enumerate(col):
            ax.annotate(
                txt,
                (col1[i], col2[i]),
                xytext=(col1[i]   0.0001, col2[i]   0.0001),
                size=12,
            )

        ax.set_xlabel(kind[kind1], size=15)
        ax.set_ylabel(kind[kind2], size=15)
        ax.set_title(f"{feature}")

    return fig, axes


fig_attack, axes_attack = plot_scatter("Attack")
fig_volume, axes_volume = plot_scatter("Volume")
plt.show()
fig_attack.savefig("test_attack.png")
fig_volume.savefig("test_volume.png")

Result:

Original answer

IIUC, you can use itertools.combinations() to create combinations of the x, y and z and plot the data. I have combined both plots into one, which looks better IMO. However, you could separate them if needed.

import pandas as pd
import matplotlib.pyplot as plt
import itertools
import numpy as np

fig, axes = plt.subplots(1, 2, figsize=(15, 10))

food = {"col1": [1, 2], "col2": [3, 4]}
food_df = pd.DataFrame(data=food)

meat = {"col1": [3, 6], "col2": [1, 2]}
meat_df = pd.DataFrame(data=meat)

vege = {"col1": [5, 9], "col2": [0, 3]}
vege_df = pd.DataFrame(data=vege)
# Here is my current codes

kind = ["fooood", "meats", "vegea"]
features = ["Attack", "Volume"]
kind1 = 1
kind2 = 2
col = ["dd", "aa"]


def plot_scatter(feature):
    ind = features.index(feature)
    x = food_df.iloc[ind]
    y = meat_df.iloc[ind]
    z = vege_df.iloc[ind]
    combinations = list(itertools.combinations((x, y, z), 2))

    ax = axes[ind]

    for col1, col2 in combinations:
        for i, c in enumerate(np.unique(col)):
            ax.scatter(col1, col2, c="b")
        for i, txt in enumerate(col):
            ax.annotate(
                txt,
                (col1[i], col2[i]),
                xytext=(col1[i]   0.0001, col2[i]   0.0001),
                size=12,
            )

    ax.set_xlabel(kind[kind1], size=15)
    ax.set_ylabel(kind[kind2], size=15)
    ax.set_title(f"{feature}")


plot_scatter("Attack")
plot_scatter("Volume")
plt.show()
fig.savefig("test.png")

Result: