Name: 使用 Gemini AI 和 Google Colab 進行 Python 數據分析 (Python Data Analysis with Gemini AI & Google Colab)
Uploaded: 2025-04-18T05:17:53.000Z
Duration: 34 min 26 s

現在進行式

In this video, we're going to cover how to do data analysis using Python.

在本視頻中，我們將介紹如何使用 Python 進行數據分析。

to 不定詞

And if you're not a programmer, don't worry, it's not going to be scary.

如果你不是程序員，也不用擔心，這並不可怕。

未來簡單式

We'll use Google Colab for our environment.

我們將使用 Google Colab 來創建環境。

And the reason I'm using Google Colab here is, of course, we can install all of this on our local computers and have all the libraries installed and everything.

當然，我在這裡使用 Google Colab 的原因是，我們可以在在地計算機上安裝所有這些軟件，並安裝所有庫和其他一切。

But for a typical user, if you're just starting out, it's probably going to be a lot of things to do and a lot of things to learn.

但對於普通用戶來說，如果你剛開始使用，可能會有很多事情要做，很多東西要學。

So that's why you can simply just go to colab.research.google.com and that will take you to Google Colab.

是以，你只需訪問 colab.research.google.com，就能進入 Google Colab。

And you can basically just start a new notebook.

基本上，你可以新建一個筆記本。

And this basically gives you a virtual server with Python installed with all the necessary libraries so you can do everything right away and get to the most interesting part.

這基本上為您提供了一個安裝了 Python 和所有必要庫的虛擬服務器，是以您可以立即執行所有操作，進入最有趣的部分。

And to top all of that, it also has Gemini, which is basically Google's AI, built into it, which will help you with a lot of the code.

此外，它還內置了雙子座，也就是谷歌的人工智能，可以幫助你完成很多代碼。

Now, first of all, to analyze data, we need some data.

首先，要分析數據，我們需要一些數據。

所以，讓我們繼續前進，獲取一些數據。

Now, you could get this data from a lot of different sources.

現在，你可以從很多不同的管道獲得這些數據。

You could get it from Excel, from Google Sheets, from SQL databases.

您可以從 Excel、Google Sheets 或 SQL 數據庫中獲取數據。

We'll just use a CSV file, which you could, of course, get it from Excel and Google Sheets too.

我們將使用 CSV 文件，當然也可以從 Excel 和 Google Sheets 中獲取。

So what I'm going to do here, I'm going to go to data.cityofchicago.org.

是以，我要做的就是訪問 data.cityofchicago.org。

There are some good datasets you can download here and use.

您可以在這裡下載並使用一些很好的數據集。

And if we scroll down a little bit here, you can see here there's this browse the data section.

如果我們向下滾動一點，可以看到這裡有瀏覽數據部分。

I'm going to go to this transportation section here.

And here on the left side, I'm going to narrow this down to datasets.

在左側，我要縮小數據集的範圍。

And see the first one that comes up here, at least right now, is this traffic crashes.

至少現在，首先出現的就是交通事故。

So I'm just going to go ahead and click on this.

And if I scroll down a little bit, we're going to see how big of a dataset this is.

如果我向下滾動一點，我們就會看到這個數據集有多大。

So you can see here, this is 925,000 rows and 48 columns.

是以，你可以看到這裡有 925 000 行和 48 列。

So this is the dataset I'm going to use to give you an example how we can use this Google Colab to actually run some data analysis on this.

是以，我將使用這個數據集為大家舉例說明如何使用 Google Colab 進行數據分析。

Now, first of all, let's download this data.

現在，首先讓我們下載這些數據。

And here there is this download file, as you can see, as an option.

如你所見，這裡有一個下載文件選項。

This may take a little bit, so I'm just going to pause this video for a little bit.

這可能需要一點時間，所以我要暫停一下視頻。

Over here, you can see it's 479 megabyte file.

在這裡，你可以看到 479 兆字節的文件。

You could use datasets much bigger than this for this analysis.

您可以使用比這大得多的數據集進行分析。

But I'm just going to use this one as an example.

Now, first, what I'm going to do, I'm going to basically upload this file to Google Colab environment.

現在，首先，我要做的是把這個文件上傳到 Google Colab 環境中。

And on the left side here, see, we have a few different sections.

在左側，你看，我們有幾個不同的部分。

If I open this first one, it gives me this.

如果我打開第一個文件，就會顯示這個資訊。

Then there are a few different sections like this.

然後有幾個不同的部分，就像這樣。

If I go to this folder icon, this is where we can actually upload a file.

如果我轉到這個文件夾圖標，我們就可以在這裡上傳文件。

Now, if you want to continuously run analysis on this data going forward, you typically wouldn't want to upload this here to Google Colab because this is a temporary environment and it's going to remove this file once you refresh this.

現在，如果你想繼續對這些數據進行分析，通常不會把它上傳到 Google Colab，因為這是一個臨時環境，一旦你刷新它，它就會刪除這個文件。

So typically, you would probably upload it to your Google Drive and then mount that Google Drive to this project so you can next time just come back to it and use it.

是以，通常情況下，你可能會將其上傳到 Google Drive，然後將 Google Drive 掛載到這個項目中，這樣下次你就可以回來使用它了。

Here, for simplicity, I'm just going to go ahead and upload it the regular way.

在這裡，為了簡單起見，我將以常規方式上傳。

Here you can see this is that file traffic.

在這裡你可以看到，這就是文件流量。

所以，我只需點擊這個，點擊 "打開"。

And this is warning me about what I just told you about that this is a temporary environment.

這就像我剛才告訴你的那樣，警告我這是一個暫時的環境。

Once you stop this runtime, it's basically going to remove the file, which is fine.

一旦停止運行，基本上就會刪除文件，這沒什麼問題。

Now, down here, if you look, it's basically creating this little circle.

現在，在這裡，如果你看一下，它基本上是在創建這個小圓圈。

同樣，這是一個相當大的文件。

Once again, I'll pause this and resume once this is done uploading this file.

我將再次暫停，上傳文件完成後再繼續。

So let's get to doing some data analysis.

那麼，讓我們來做一些數據分析吧。

So if you do any data analysis using Python, you are going to end up dealing with a library called pandas.

是以，如果你使用 Python 進行任何數據分析，最終都會與一個名為 pandas 的庫打交道。

And that's the first thing we're going to do.

We're going to start by adding a line here.

And typically, when people import pandas library, they rename it as PD.

通常，人們在導入 pandas 庫時，會將其重命名為 PD。

And basically, what this does, it makes it easy later on to just type PD instead of typing pandas.

基本上，這樣做的目的是方便以後直接鍵入 PD，而不是鍵入 pandas。

And you could keep listing multiple lines in this code block.

你可以在這個代碼塊中不斷列出多行。

But typically, sometimes I like to separate different blocks here.

但通常情況下，有時我喜歡在這裡分隔不同的區塊。

And it's going to add another block of code right here.

它將在這裡添加另一個代碼塊。

So now we ended up importing this pandas library under variable PD.

是以，我們最終在變量 PD 下導入了這個 pandas 庫。

So the next part is going to be to actually grab that CSV file and all the data that's in that CSV file, which is this.

是以，下一步就是抓取 CSV 文件和 CSV 文件中的所有數據，也就是這個。

現在，這個文件有了這個瘋狂的名字。

So I'm going to just go ahead and rename this.

是以，我只需點擊這裡並重新命名。

And I'll just basically just rename this to something easier, something like that.

我基本上會把這個重命名為更簡單的名字，類似這樣。

And that should be the name of the file now.

So now this point, to get to that file, we're going to do PD dot, which is this PD variable from here.

是以，現在這一點，要進入該文件，我們要做的是 PD dot，也就是這裡的 PD 變量。

And you can see here it suggests read CSV.

Now, there are many different file types you can do here.

現在，您可以在這裡處理多種不同的文件類型。

So if I type read, you can see there's like read Excel, CSV, clipboard, HTML, JSON.

是以，如果我輸入讀取，你可以看到有讀取 Excel、CSV、剪貼板、HTML、JSON 等。

And there are all these different formats.

在我們的案例中，這是一個 CSV 文件。

So I'm going to go read CSV, open parentheses.

And then in parentheses, in quotes, we're going to basically enter the name of this file right here.

然後在括號內的引號中，我們基本上要在這裡輸入這個文件的名稱。

You want to make sure you match it exactly.

So this is the file name, traffic crashes from here.

這就是文件名，交通事故就是從這裡開始的。

我們需要將其保存在一個變量中。

所以，我們要走在這條線的前面。

And we're going to make some variable name and make it equal this.

我們將創建一個變量名，使其等於這個變量名。

Now, typically, the variables people call here is going to be something like DF, short for data frame.

通常，人們在這裡調用的變量會是類似 DF 這樣的變量，即數據幀的縮寫。

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使用 Gemini AI 和 Google Colab 進行 Python 數據分析 (Python Data Analysis with Gemini AI & Google Colab)

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