分别绘制所有 Pandas 数据框列
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Plot all pandas dataframe columns separately
提问by Manu Sharma
I have a pandas dataframe who just has numeric columns, and I am trying to create a separate histogram for all the features
我有一个只有数字列的 Pandas 数据框，我正在尝试为所有特征创建一个单独的直方图
ind group people value value_50
1 1 5 100 1
1 2 2 90 1
2 1 10 80 1
2 2 20 40 0
3 1 7 10 0
3 2 23 30 0
but in my real life data there are 50+ columns, how can I create a separate plot for all of them
但在我的现实生活数据中有 50 多列，我如何为所有这些列创建一个单独的图
I have tried
我试过了
df.plot.hist( subplots = True, grid = True)
It gave me an overlapping unclear plot.
它给了我一个重叠的不清楚的情节。
how can I arrange them using pandas subplots = True. Below example can help me to get graphs in (2,2) grid for four columns. But its a long method for all 50 columns
我如何使用 pandas subplots = True 来安排它们。下面的示例可以帮助我在 (2,2) 网格中获取四列的图形。但对于所有 50 列来说，这是一个很长的方法
fig, [(ax1,ax2),(ax3,ax4)] = plt.subplots(2,2, figsize = (20,10))
回答by ImportanceOfBeingErnest
Pandas subplots=Truewill arange the axes in a single column.
Pandassubplots=True会将轴排列在单列中。
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame(np.random.rand(7,20))
df.plot(subplots=True)
plt.tight_layout()
plt.show()
Here, tight_layoutisn't applied, because the figure is too small to arange the axes nicely. One can use a bigger figure (figsize=(...)) though.
此处tight_layout未应用，因为图太小而无法很好地排列轴。不过，可以使用更大的数字 ( figsize=(...))。
In order to have the axes on a grid, one can use the layoutparameter, e.g.
为了将轴放在网格上，可以使用layout参数，例如
df.plot(subplots=True, layout=(4,5))
The same can be achieved if creating the axes via plt.subplots()
如果通过以下方式创建轴，也可以实现相同的目的 plt.subplots()
fig, axes = plt.subplots(nrows=4, ncols=5)
df.plot(subplots=True, ax=axes)
回答by annhak
If you want to plot them separately (which is why I ended up here), you can use
如果你想分别绘制它们（这就是我在这里结束的原因），你可以使用
for i in df.columns:
plt.figure()
plt.hist(df[i])
回答by Ramon
An alternative for this task can be using the "hist" method with hyperparameter "layout". Example using part of the code provided by @ImportanceOfBeingErnest:
此任务的替代方法是使用具有超参数“布局”的“hist”方法。使用@ImportanceOfBeingErnest 提供的部分代码的示例：
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame(np.random.rand(7,20))
df.hist(layout=(5,4), figsize=(15,10))
plt.show()
