Python 如何使用 Pandas DF 绘制计数条形图，按一个分类列分组并按另一个分类着色
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How to plot a count bar chart with a Pandas DF, grouping by one categorical column and colouring by another
提问by tlanigan
I have a dataframe that looks roughly like this:
我有一个大致如下所示的数据框：
Property Name industry
1 123 name1 industry 1
1 144 name1 industry 1
2 456 name2 industry 1
3 789 name3 industry 2
4 367 name4 industry 2
. ... ... ...
. ... ... ...
n 123 name1 industry 1
I want to make a bar plot that plots how many rows for each of the Names there are, and colors the bars by what industry it is. I've tried something like this:
我想制作一个条形图，绘制每个 Names 的行数，并根据它的行业为条形着色。我试过这样的事情：
ax = df['name'].value_counts().plot(kind='bar',
figsize=(14,8),
title="Number for each Owner Name")
ax.set_xlabel("Owner Names")
ax.set_ylabel("Frequency")
I get the following:
我得到以下信息：
My question is how do I colour the bars according the the industry column in the dataframe (and add a legend).
我的问题是如何根据数据框中的行业列为条形着色（并添加图例）。
Thanks!
谢谢！
回答by tlanigan
This is my answer:
这是我的回答：
def plot_bargraph_with_groupings(df, groupby, colourby, title, xlabel, ylabel):
"""
Plots a dataframe showing the frequency of datapoints grouped by one column and coloured by another.
df : dataframe
groupby: the column to groupby
colourby: the column to color by
title: the graph title
xlabel: the x label,
ylabel: the y label
"""
import matplotlib.patches as mpatches
# Makes a mapping from the unique colourby column items to a random color.
ind_col_map = {x:y for x, y in zip(df[colourby].unique(),
[plt.cm.Paired(np.arange(len(df[colourby].unique())))][0])}
# Find when the indicies of the soon to be bar graphs colors.
unique_comb = df[[groupby, colourby]].drop_duplicates()
name_ind_map = {x:y for x, y in zip(unique_comb[groupby], unique_comb[colourby])}
c = df[groupby].value_counts().index.map(lambda x: ind_col_map[name_ind_map[x]])
# Makes the bargraph.
ax = df[groupby].value_counts().plot(kind='bar',
figsize=FIG_SIZE,
title=title,
color=[c.values])
# Makes a legend using the ind_col_map
legend_list = []
for key in ind_col_map.keys():
legend_list.append(mpatches.Patch(color=ind_col_map[key], label=key))
# display the graph.
plt.legend(handles=legend_list)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
回答by TYZ
It might be a little bit too complicated but this does the work. I first defined the mappings from name to industry and from industry to color (it seems like there are only two industries but you can adjust the dictionary to your case):
这可能有点太复杂了，但这确实有效。我首先定义了从名称到行业以及从行业到颜色的映射（似乎只有两个行业，但您可以根据自己的情况调整字典）：
ind_col_map = {
"industry1": "red",
"industry2": "blue"
}
unique_comb = df[["Name","industry"]].drop_duplicates()
name_ind_map = {x:y for x, y in zip(unique_comb["Name"],unique_comb["industry"])}
Then the color can be generated by using the above two mappings:
然后就可以使用上面的两个映射来生成颜色了：
c = df['Name'].value_counts().index.map(lambda x: ind_col_map[name_ind_map[x]])
Finally, you only need to simply add colorto your plotting function:
最后，您只需要简单地添加color到绘图函数中：
ax = df['Name'].value_counts().plot(kind='bar',
figsize=(14,8),
title="Number for each Owner Name", color=c)
ax.set_xlabel("Owner Names")
ax.set_ylabel("Frequency")
plt.show()
回答by Scott Boston
Let's use a some dataframe reshaping and matplotlib:
让我们使用一些数据帧整形和 matplotlib：
ax = df.groupby(['industry','Name'])['Name'].count().unstack(0).plot.bar(title="Number for each Owner Name", figsize=(14,8))
_ = ax.set_xlabel('Owner')
_ = ax.set_ylabel('Frequency')
Output:
输出：
