pandas 熊猫 - 绘制排序列以增加整数索引
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pandas - plot sorted column to increasing integer index
提问by nivniv
Let's say I have a pandas series with numerical values. What's the shortest way to plot the sorted series against an increasing integer index?
假设我有一个带有数值的Pandas系列。根据递增的整数索引绘制排序序列的最短方法是什么？
The plot should show:
该图应显示：
x-axis: 0,1,2,3,4,...
x 轴：0,1,2,3,4,...
y-axis: the sorted values of the series.
y 轴：系列的排序值。
(please notice that I cannot plot it against the series' index, because the index is not necessarily an increasing index. In my case it's some id that I use for different reasons)
（请注意，我无法根据系列的索引绘制它，因为该索引不一定是递增的索引。在我的情况下，它是我出于不同原因使用的某个 id）
Thanks
谢谢
回答by paulperry
This will first sort the series and then plot, ignoring the index of the series:
这将首先对系列进行排序，然后绘制，忽略系列的索引：
ts = pd.Series(np.random.randn(100), index=pd.date_range('1/1/2000', periods=100))
ts.sort_values().plot(use_index=False)
ts = pd.Series(np.random.randn(100), index=pd.date_range('1/1/2000', periods=100))
ts.sort_values().plot(use_index=False)
回答by chris-sc
Quick and easy: You can add a column with increasing integers and use this as x-values:
快速简便：您可以添加一个整数递增的列，并将其用作 x 值：
# some dataframe df
df['int_index'] = range(len(df))
df.plot(x='int_index', y='sorted_values')
If you don't want to keep the index, drop it afterwards:
如果您不想保留索引，请在之后删除它：
df.drop('int_index', axis=1, inplace=True)
Helper function:
辅助功能：
The Pandas plotfunction does not take "external" data as indices. You can use matplotlib directly to plot as tnknepp showed, or keep the Pandas plotting (and formatting) with a helper function:
Pandasplot函数不会将“外部”数据作为索引。您可以直接使用 matplotlib 进行绘图，如 tnknepp 所示，或使用辅助函数保持 Pandas 绘图（和格式化）：
def plot_sorted(series, **kwargs):
df = pd.DataFrame({'data': series}).reset_index(drop=True)
df.plot(**kwargs)
Using a wrapper like this you can quickly plot any Seriesand customize the plot by calling with the appropriate arguments that will be used to call the plotmethod. Examples:
使用这样的包装器，您可以快速绘制任何Series图形，并通过使用将用于调用plot方法的适当参数进行调用来自定义绘图。例子：
ts = pd.Series(np.random.randn(100), index=pd.date_range('1/1/2000', periods=100))
# default pandas plot (with integer indices)
plot_sorted(ts)
# scatter plot (using `data` for x and y)
plot_sorted(ts, x='data', y='data', kind="scatter")
回答by tnknepp
For one line:
对于一行：
import pandas as pd
from pylab import *
fig = figure( figsize=(5,5) )
ax = fig.add_subplot(111)
ax.plot( np.arange(df.shape[0]), df.sort(['A']).A )
This is assuming your dataframe is called df and you want to plot the data stored in column "A".
这是假设您的数据框称为 df 并且您想要绘制存储在“A”列中的数据。
