matplotlib的plot()和pandas plot()之间的区别? [英] Difference between matplotlib's plot() and pandas plot()?

问题描述

matplotlib.pyplot.plot()和pandas.DataFrame.plot()有什么区别?

我们可以同时使用两者进行绘图，但是两者之间的度量差异是多少?

We can plot using both but what is the measure difference between both?

如何绘制条形图并按分类变量分组?

How can i draw bar chart and group by some categorical variable?

推荐答案

Matplotlib的pyplot是熊猫在其plot函数中使用的库.熊猫的情节只是方便的捷径. 对于条形图的问题:我建议使用Seaborn的 barplot ，类别为色调. 如果您只想使用熊猫，那么也许是这样的:

Matplotlib's pyplot is the library that Pandas use in their plot function. Pandas' plot is only a convenient shortcut. For the bar chart question: I would suggest using Seaborn's barplot, using the desired category as hue. If you wish to only use Pandas, then maybe something like:

df = pd.DataFrame(np.random.rand(10, 1), columns=['col_name'])
df['category'] = df.col_name>0.5

print(df)

   col_name  category
0  0.053908     False
1  0.136295     False
2  0.325790     False
3  0.362942     False
4  0.919924      True
5  0.406884     False
6  0.433959     False
7  0.725699      True
8  0.582537      True
9  0.608040      True

df.groupby('category').count().plot(kind='bar', legend=False)

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