pandas 如何使用尽可能少的代码在 Jupyter notebook 中使用 Python 创建给定数据的频率分布表？
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How to create a frequency distribution table on given data with Python in Jupyter notebook with as few code as possible?
提问by Mainul Islam
Develop a frequency distribution summarizing this data.This data is a demand for an object over a period of 20 days.
开发总结此数据的频率分布。此数据是一个对象在 20 天内的需求。
2 1 0 2 1 3 0 2 4 0 3 2 3 4 2 2 2 4 3 0. The task is to create a table in the jupyter notebook with columns Demand and Frequency. Note: Demand has to be in ascending order. This is what I did.
2 1 0 2 1 3 0 2 4 0 3 2 3 4 2 2 2 4 3 0. 任务是在 jupyter notebook 中创建一个表格，其中包含需求和频率列。注意：需求必须按升序排列。这就是我所做的。
list_of_days = [2, 1, 0, 2, 1, 3, 0, 2, 4, 0, 3, 2 ,3, 4, 2, 2, 2, 4, 3, 0] # created a list of the data
import pandas as pd
series_of_days = pd.Series(list_of_days) # converted the list to series
series_of_days.value_counts(ascending = True) # the frequency was ascending but not the demand
test = dict(series_of_days.value_counts())
freq_table = pd.Series(test)
pd.DataFrame({"Demand":freq_table.index, "Frequency":freq_table.values})
The output has to be like this:
输出必须是这样的：
<table border = "1">
<tr>
<td>Demand</td>
<td>Frequency</td>
</tr>
<tr>
<td>0</td>
<td>4</td>
</tr>
<tr>
<td>1</td>
<td>2</td>
</tr>
<tr>
<td>2</td>
<td>7</td>
</tr>
<table>
and so on. Is there a better way to shorten the Python code? Or make it more efficient?
等等。有没有更好的方法来缩短 Python 代码？还是让它更有效率？
回答by jezrael
You can use value_countswith reset_indexand sorting by sort_values:
您可以使用value_countswithreset_index和排序方式sort_values：
df1 = pd.Series(list_of_days).value_counts()
.reset_index()
.sort_values('index')
.reset_index(drop=True)
df1.columns = ['Demand', 'Frequency']
print (df1)
Demand Frequency
0 0 4
1 1 2
2 2 7
3 3 4
4 4 3
Another similar solution with sorting by sort_index:
排序方式的另一个类似解决方案sort_index：
df1 = pd.Series(list_of_days)
.value_counts()
.sort_index()
.reset_index()
.reset_index(drop=True)
df1.columns = ['Demand', 'Frequency']
print (df1)
Demand Frequency
0 0 4
1 1 2
2 2 7
3 3 4
4 4 3
回答by Mohammad Athar
import collections
collections.Counter(list_of_days)
Should do what you're describing
应该做你所描述的
回答by piRSquared
I'm going for the literal creation of the HTML table you posted
我要创建您发布的 HTML 表格
pd.value_counts([2,1,0,2,1,3,0,2,4,0,3,2,3,4,2,2,2,4,3,0]).to_frame(name='Frequency').rename_axis('Demand', 1).sort_index()
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>Demand</th>
<th>Frequency</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>4</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
</tr>
<tr>
<th>2</th>
<td>7</td>
</tr>
<tr>
<th>3</th>
<td>4</td>
</tr>
<tr>
<th>4</th>
<td>3</td>
</tr>
</tbody>
</table>
回答by Po Stevanus Andrianta
if you want shortest, probably this code, Counter by default will sort the key in ascending.
如果你想要最短的，可能是这个代码，默认情况下 Counter 会按升序对键进行排序。
list_of_days = [2, 1, 0, 2, 1, 3, 0, 2, 4, 0, 3, 2, 3, 4, 2, 2, 2, 4, 3, 0]
day_counter = Counter(list_of_days).items()
data = [ [a,b] for a,b in day_counter ]
print(data)
[[0, 4], [1, 2], [2, 7], [3, 4], [4, 3]]
[[0, 4], [1, 2], [2, 7], [3, 4], [4, 3]]
