pandas 单变量类别散点图熊猫
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原文地址: http://stackoverflow.com/questions/37194968/
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Single variable category scatter plot pandas
提问by Sitz Blogz
Is It possible to plot single value as scatter plot? I can very well plot it in line by getting the ccdfs with markers but I want to know if any alternative is available?
是否可以将单个值绘制为散点图？我可以通过使用标记获取 ccdfs 来很好地绘制它，但我想知道是否有任何替代方法可用？
Input:
输入：
Input 1
输入 1
tweetcricscore 51 high active
Input 2
输入 2
tweetcricscore 46 event based
tweetcricscore 12 event based
tweetcricscore 46 event based
Input 3
输入 3
tweetcricscore 1 viewers
tweetcricscore 178 viewers
Input 4
输入 4
tweetcricscore 46 situational
tweetcricscore 23 situational
tweetcricscore 1 situational
tweetcricscore 8 situational
tweetcricscore 56 situational
I can very much write scatter plot code with bokehand pandasusing xand yvalues. But in case of single value ?
我可以用非常写散点图代码bokeh并pandas使用x和y值。但是在单个值的情况下？
When all the inputs are merged as one input and are to be grouped by col[3], values are col[2].
当所有输入合并为一个输入并按 分组时col[3]，值为col[2]。
The code below is for data set with 2 variables
下面的代码适用于具有 2 个变量的数据集
import numpy as np
import matplotlib.pyplot as plt
from pylab import*
import math
from matplotlib.ticker import LogLocator
import pandas as pd
from bokeh.charts import Scatter, output_file, show
df = pd.read_csv('input.csv', header = None)
df.columns = ['col1','col2','col3','col4']
scatter = Scatter( df, x='col2', y='col3', color='col4', marker='col4', title='plot', legend=True)
output_file('output.html', title='output')
show(scatter)
Sample Output
样本输出
采纳答案by MaxU
UPDATE:
更新：
look at Bokehand Seaborngalleries - it might help you to understand what kind of plot fits your needs
看看Bokeh和Seaborn画廊——它可能会帮助你了解什么样的情节适合你的需要
you may try violinplot like this:
你可以像这样尝试 violinplot：
sns.violinplot(x="category", y="val", data=df)
or HeatMaps:
或热图：
import numpy as np
import pandas as pd
from bokeh.charts import HeatMap, output_file, show
cats = ['active', 'based', 'viewers', 'situational']
df = pd.DataFrame({'val': np.random.randint(1,100, 1000), 'category': np.random.choice(cats, 1000)})
hm = HeatMap(df)
output_file('d:/temp/heatmap.html')
show(hm)
回答by Grr
You could try a boxplotor violinplot. Alternatively if you don't like these and just want a vertical distribution of dots you could force a scatter to plot along a single x value. To do this you would need to create an array of a fixed value (say 1) that is the same length as the array you will be plotting:
你可以尝试一箱线图或violinplot。或者，如果您不喜欢这些并且只想要点的垂直分布，您可以强制沿着单个 x 值绘制散点图。为此，您需要创建一个与要绘制的数组长度相同的固定值数组（例如 1）：
ones = []
for range(len(data)):
ones.append(1)
plt.scatter(ones,data)
plt.show()
That will give you something like this:
这会给你这样的东西：
回答by H_J
You can plot index on x-axis and column value on y-axis
您可以在 x 轴上绘制索引，在 y 轴上绘制列值
df = pd.DataFrame(np.random.randint(0,10,size=(100, 1)), columns=list('A'))
sns.scatterplot(data=df['A'])
回答by Yaakov Bressler
Something I use rather regularly is a "size plot" – a visualization similar to the one you're requesting where a single feature can be compared across groups. Here is an example using your data:
我经常使用的是“大小图”——一种类似于您请求的可视化，其中可以跨组比较单个特征。以下是使用您的数据的示例：
Here is the code to achieve this size plot:
这是实现此大小图的代码：
fig, ax = plt.subplots(1,1, figsize=(8,5))
colors = ['blue','green','orange','pink']
yticks = {"ticks":[],"labels":[]}
xticks = {"ticks":[],"labels":[]}
agg_functions = ["mean","std","sum"]
# Set size plot
for i, (label, group_df) in enumerate(df.groupby('type', as_index=False)):
# Set tick
yticks["ticks"].append(i)
yticks["labels"].append(label)
agg_values = group_df["tweetcricscore"].aggregate(agg_functions)
for ii, (agg_f, x) in enumerate(agg_values.iteritems()):
ax.scatter(x=ii, y = i, label=agg_f, s=x, color=colors[i])
# Add your x axis
if ii not in xticks["ticks"]:
xticks["ticks"].append(ii)
xticks["labels"].append(agg_f)
# Set yticks:
ax.set_yticks(yticks["ticks"])
ax.set_yticklabels(yticks["labels"], fontsize=12)
ax.set_xticks(xticks["ticks"])
ax.set_xticklabels(xticks["labels"], fontsize=12)
plt.show()
