熊猫数据框的多个条形图

Question

So I have a data frame like

exp_name, index, items, clicks
"foo",0, "apple",200
"foo",0, "banana", 300
"foo",0,"melon",220
"foo",1, "apple", 10
"foo",1,"peach", 20
"bar",0, "apple",400
"bar",0,'banana', 500
"bar",0, "melon",240
"bar",1,"apple",500

and so on

I want to plot... for each experiment name... bar plots of number of clicks for each item in each index but colored by index. So basically.. plot 1.. for experiment "foo", a bar plots.. where index == 0.. all the barplots for index 0 in one color.. index 1 in another color.

if the item is missing (for example peach is in "foo", 1 but not in any other place) replace "peach" to be zero in other places.

Answer 1

I copy/paste your data into a txt file called 'test.txt' and rename "index" as "status" to avoid confusion with the DataFrame index. Then I use the Seaborn library to make barplots with the contingencies you mention (and as I understand them). I use subplots rather than use color to set apart "status" cause I personally think it looks cleaner, but I use colors below since that's what you asked about.

import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

df = pd.read_csv('test.txt')  
fig, ax = sns.plt.subplots(1, 1, figsize=(7,5))
sns.factorplot(x="items", y="clicks", hue="exp_name", col="status", data=df, kind="bar")
plt.show()

Gives the following:

If you really want to distinguish "index" (what I call "status") by color, you might define a new variable which combines "exp_name" with "status"

df['exp'] = df.exp_name + df.status.astype(str)
sns.factorplot(x="items", y="clicks", hue="exp", data=df, kind="bar")

Gives something like this

Check out the docs for seaborn if you have more questions. It's a really great library for categorical data. Changing the legend labels and other settings follows matplotlib conventions.