問題描述

截至 2018 年 8 月 9 日，Power BI 支持 Python 可視化.他們以前支持 R 可視化，但我仍然覺得這些集成有點尷尬.讓我告訴你我的意思:

假設您有一個包含時間序列數據的表，其中第一行包含名稱日期"和值"，內容分別是格式為 yyyy-mm-dd 的日期和一個數字:

日期、數值2017-01-12,12017-01-13,42017-01-14,22017-01-15,42017-01-16,22017-01-17,22017-01-18,22017-01-19,52017-01-20,52017-01-21,52017-01-22,52017-01-23,62017-01-24,32017-01-25,62017-01-26,62017-01-27,52017-01-28,82017-01-29,42017-01-30,2

如果您將該數據集存儲為像 timerseries.csv 這樣的文本文件并使用 Get Data | 導入它.文本/CSV，你會得到一個比 VISUALIZATIONS |字段，像這樣:

您可以使用 VISUALIZATIONS | 檢查您的表表 并獲取:

有了這個設置，你應該認為你已經準備好使用這個漂亮的新功能來釋放 Py VISUALIZATION 的力量了:

如果你點擊它，你會得到這個:

你被告知

將字段拖入可視化"窗格中的值"區域以開始腳本

如果你從 Value 開始，你會在編輯器中得到這個默認設置:

如果您按照 Power BI 團隊在

但這就是我目前的結局.

如果編輯器中的默認數據框共享標準數據框的功能，您應該能夠引用該數據框中的列并使用此代碼段輕松繪制圖表:

import matplotlib.pyplot as pltplt.plot(數據集['值'])plt.show()

但是當你運行它時，它只會返回一個錯誤:

至少可以說細節很詳細.

我也嘗試過同時導入 Dates 和 Values，并嘗試使用 dataset.plot()，但似乎沒有任何效果.我還嘗試通過這種方式將日期層次結構分解為簡單的日期:

那么，對數據格式、導入方法和/或代碼片段有什么想法嗎?

感謝您的任何建議！

編輯 1 - 按照 Foxan Ng 的回答:

在值"字段中添加兩列:

這仍然返回一個錯誤

TypeError: from_bounds() 接受 4 個位置參數，但給出了 6 個

我沒有遇到你提到的錯誤.您是否將兩列都放入 Values?

import matplotlib.pyplot as pltplt.plot(數據集['日期']，數據集['值'])plt.show()

使用 M 查詢更新:

讓Source = Csv.Document(File.Contents("C:your-directory..	imerseries.csv"),[Delimiter=",", Columns=2, Encoding=1252, QuoteStyle=QuoteStyle.None]),#"PromoteHeaders" = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),#"Changed Type" = Table.TransformColumnTypes(#"Promoted Headers",{{"Date", type date}, {"Value", Int64.Type}})在#改變類型"

As of today, August 9 2018, Power BI supports Python Visualizations. They've had support for R Visualizations before, but I still find these integrations to be a bit awkward. Let me show you what I mean:

Let's say that you have a table with time series data, where the top row containts the names 'Date' and 'Value', and the contents are dates of the form yyyy-mm-dd and a number, respectively:

Date,Value
2017-01-12,1
2017-01-13,4
2017-01-14,2
2017-01-15,4
2017-01-16,2
2017-01-17,2
2017-01-18,2
2017-01-19,5
2017-01-20,5
2017-01-21,5
2017-01-22,5
2017-01-23,6
2017-01-24,3
2017-01-25,6
2017-01-26,6
2017-01-27,5
2017-01-28,8
2017-01-29,4
2017-01-30,2

If you store that dataset as a textfile like timerseries.csv and import it using Get Data | Text/CSV, you get a table uner VISUALIZATIONS | FIELDS, like this:

You can inspect your table using VISUALIZATIONS | Table and get:

With this setup, one should think that you were all set for unleashing the power of a Py VISUALIZATION using this beautiful new feature:

If you click that, you get this:

And you're told to

Drag fields into the Values area in the Visualization pane to start scripting

If you start with Value, you get this default setup in the editor:

And if you follow the instructions given by the Power BI team in the August 2018 feature summary you should be able to make a matplotlib plot quite easily.

But this is where it ends for me at the time being.

If the default dataframe in the editor shares the features of a standard dataframe, you should be able to reference a column in that dataframe and easily make a plot with this snippet:

import matplotlib.pyplot as plt
plt.plot(dataset['Value'])
plt.show()

But when you run it, it onlu returns an error:

And the details are elaborate to say the least.

I've also tried to import both Dates and Values, and I've tried plotting the dataframe directly with dataset.plot(), but nothing seems to be working. I've also tried stripping the date hierarchy down to simple dates this way:

So, any ideas on the dataformat, import method and/or the snippet?

Thank you for any suggestions!

EDIT 1 - Following the answer from Foxan Ng:

Add both columns in the Value field:

This still returns an error edning with:

TypeError: from_bounds() takes 4 positional arguments but 6 were given

I didn't encounter errors that you've mentioned. Have you dropped in both columns into Values?

import matplotlib.pyplot as plt
plt.plot(dataset['Date'], dataset['Value'])
plt.show()

UPDATED with M query:

let
    Source = Csv.Document(File.Contents("C:your-directory..	imerseries.csv"),[Delimiter=",", Columns=2, Encoding=1252, QuoteStyle=QuoteStyle.None]),
    #"Promoted Headers" = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),
    #"Changed Type" = Table.TransformColumnTypes(#"Promoted Headers",{{"Date", type date}, {"Value", Int64.Type}})
in
    #"Changed Type"

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