Name: 使用 Pandas Python 進行探索性數據分析 (Exploratory Data Analysis with Pandas Python)
Uploaded: 2025-05-26T18:34:18.000Z
Duration: 40 min 22 s

So, you've found a new dataset that you're excited to explore.

是以，您發現了一個新的數據集，很想探索一下。

程度副詞

Or maybe you just want to get familiar with using Python for data analysis.

或者，您只是想熟悉使用 Python 進行數據分析。

Well, in this video, I'll show you some of the essential skills that you'll need to know in order to perform data analysis with Python and Pandas.

在本視頻中，我將向您展示使用 Python 和 Pandas 進行數據分析所需的一些基本技能。

Hi, my name is Rob, and I'm a data scientist and three-time Kaggle Grandmaster.

大家好，我叫羅布，是一名數據科學家，也是三屆 Kaggle 大師。

I've spent a lot of my time exploring new datasets on Kaggle.

我花了很多時間在 Kaggle 上探索新的數據集。

In this video, I'm going to walk through step-by-step the process I usually take when exploring a brand new dataset.

在本視頻中，我將逐步介紹我在探索全新數據集時通常會採用的流程。

If you're completely new to using Python and Pandas, I suggest you first watch my tutorial on an introduction to Pandas.

如果你是使用 Python 和 Pandas 的新手，我建議你先觀看我的 Pandas 入門教程。

In this video, I'll be working entirely in a Kaggle notebook.

在本視頻中，我將完全在 Kaggle 筆記本中工作。

So this means if you don't have Python already set up on your computer, you don't need to  Just click the link to the Kaggle notebook.

是以，這意味著如果您的電腦上沒有安裝 Python，您就不需要點擊 Kaggle 筆記本的鏈接。

You can fork or copy it, and then step through the code along with me or after this video is over.

你可以分叉或複製它，然後和我一起或在視頻結束後逐步學習代碼。

Okay, so here we are in a Kaggle notebook.

好了，現在我們來到了 Kaggle 筆記本中。

Kaggle notebook is very similar to a Jupyter notebook, just an environment where we can run code and write some descriptions around it in plain text.

Kaggle 筆記本與 Jupyter 筆記本非常相似，只是一個我們可以運行代碼並用純文本寫一些描述的環境。

So in general, I always import these packages.

是以，一般情況下，我都會導入這些套裝軟體。

Of course, Pandas, and we're going to import that as pd.

當然是 Pandas，我們將以 pd 的形式導入。

Then we also have NumPy, which will import NumPy as np.

然後，我們還有 NumPy，它將以 np 的形式導入 NumPy。

We're going to use a few different visualization libraries, matplotlib.

我們將使用一些不同的可視化庫，如 matplotlib。

是以，將 matplotlib 和 pylab 導入 plot。

And then we're also going to import a package called seaborn, which is really helpful for doing exploratory plots with our data.

然後，我們還將導入一個名為 seaborn 的套裝軟體，它對我們使用數據繪製探索圖很有幫助。

And we're going to import seaborn as sns.

我們將以 sns 的形式導入 seaborn。

Few other things we want to do before we get too far into this is we're going to use a style sheet for matplotlib and seaborn style plots.

在深入瞭解之前，我們還想做幾件事，那就是為 matplotlib 和 seaborn 風格繪圖使用樣式表。

So the way we do that is we do plot style use ggplot.

是以，我們的方法是使用 ggplot 進行繪圖。

Another setting we're going to add is we're going to expand the number of columns that are shown when we display a different data frame in our notebook.

我們要添加的另一個設置是，當我們在筆記本中顯示不同的數據幀時，我們要擴展顯示的列數。

是以，我們的方法是 pd 設置選項。

And then we set the max columns to, let's say, 200 just to make sure we can see them all.

然後，我們將最大列數設置為 200，以確保我們可以看到所有列數。

Now I'm going to comment that part out now and show you why it's necessary later.

現在我先把這部分註釋出來，稍後再告訴你為什麼有必要這樣做。

And now next thing we need to do is import our data.

現在，我們需要做的下一件事就是導入數據。

And with Kaggle, we can go here to this tab on the side.

而使用 Kaggle，我們可以轉到側面的這個選項卡。

What we're going to be looking at is a data set that I created with a bunch of information about over a thousand roller coasters.

我們要看的是我創建的一個數據集，其中包含有關一千多個雲霄飛車的大量資訊。

It has information like the speed of the roller coaster, what material was used to make the roller coaster, and some cool things like that.

上面有雲霄飛車的速度、製作雲霄飛車的材料等資訊，還有一些很酷的東西。

But if you're starting in a notebook and want to look at a different data set, you can always click on add data and look through a bunch of these data sets that already exist or upload your own data.

但是，如果你從一個筆記本開始，想要查看不同的數據集，你可以隨時點擊添加數據，查看一堆已經存在的數據集，或者上傳你自己的數據。

And then, of course, if you're working on a notebook on your own machine, you can just reference whatever CSV file you have.

當然，如果你在自己的電腦上使用筆記本，也可以直接引用任何 CSV 文件。

So I'm going to import our data and just call it df for data frame.

是以，我要導入我們的數據，並將其稱為 df，表示數據幀。

And we're going to use read CSV to read in the data.

我們將使用讀取 CSV 來讀取數據。

So I know this is in the input folder, and it's called CoasterDB.

所以我知道這是在輸入文件夾中，它的名字叫 CoasterDB。

So that we've read in very quickly, and we have now a data frame we can work with.

這樣，我們就可以快速讀取數據，現在就有了一個可以使用的數據幀。

So the next step is to do some basic data understanding of our data.

是以，下一步是對我們的數據進行一些基本的數據理解。

So very simple stuff, but the first thing I like to do is do a df.shape.

這些都很簡單，但我喜歡做的第一件事就是做一個 df.shape。

This tells us the shape of the data frame or the data that we just loaded in.

這將告訴我們數據幀的形狀或我們剛剛載入的數據的形狀。

In this data set, we have 187 rows and 56 columns of data.

在這個數據集中，我們有 187 行 56 列數據。

我們還可以就此運行 head 命令。

We could change the number of rows we want to see.

我們可以更改想要查看的行數。

Let's say if we want to see the first 20.

比方說，如果我們想查看前 20 位。

And now if you notice here, when we do this head command, and let's put it back at five, what we see here is it shows us all the different columns, but then at a certain point, we see just dots, and then it picks up later with the last bit of columns.

現在，如果你注意到這裡，當我們執行這個頭部命令時，讓我們把它放回五位，我們看到的是它顯示了所有不同的列，但在某一點上，我們看到的只是點，然後它又開始顯示最後一點列。

This is because pandas by default does not show you every single column in the data frame.

這是因為 pandas 默認情況下不會顯示數據幀中的每一列。

But for exploration, I find it easier to show them all.

但為了便於探索，我覺得把它們都展示出來更容易。

So going back up here to our PD set option, we're going to make this 200, which is plenty enough to see every column in our data set.

是以，回到 "數據集 "選項，我們將其設置為 200，這樣就足夠看到數據集中的每一列了。

And then rerun this head command, and we'll see all of the different columns in the data set.

然後重新運行這條 head 命令，我們就能看到數據集中的所有不同列。

Now, another thing we want to do is just list out all the columns.

現在，我們要做的另一件事就是列出所有列。

Since this data frame has a lot of columns, we can just do df.columns to see them all.

由於該數據幀有很多列，我們只需執行 df.columns 即可查看所有列。

And now we've seen all the columns listed.

現在我們已經看到了列出的所有欄目。

Eventually, if we want to subset our data set, we can remove some of these columns, and I'm going to show you how to do that later.

最後，如果我們想對數據集進行子集化，我們可以刪除其中的一些列，稍後我會向你演示如何操作。

So the next thing we might want to do is, for each of these columns, to find out actually what D type that pandas has decided it is.

是以，我們接下來可能要做的事情是，針對其中的每一列，找出 pandas 確定的 D 類型。

So if you remember from the earlier tutorial, in a pandas data frame, every column is actually a series, and every pandas series has a type.

是以，如果你還記得前面的教程，在 pandas 數據幀中，每一列實際上都是一個序列，而每個 pandas 序列都有一個類型。

So if we type in df.dtypes, we can actually see, for each of these columns, what the type of column it is.

是以，如果我們輸入 df.dtypes，就可以看到每一列的類型。

So we have a lot of objects here, which are just string-type columns, and then we have some that are float values.

是以，我們這裡有很多對象，它們只是字符串類型的列，還有一些是浮點數值。

Actually, down here at the bottom, I created this data set, and I added some of the numeric values of features, like the height of the roller coaster, the speed in miles per hour here to the end.

實際上，我在底部創建了這個數據集，並在末尾添加了一些特徵的數值，如雲霄飛車的高度和以英里/小時為組織、部門的速度。

Okay, so one of the last things I want to show you here is just the describe function.

好了，最後要給大家展示的就是描述功能。

So if you type in df.describe, there we go, what it'll show us is some information and statistics about the numeric data in our data set.

是以，如果輸入 df.d describe，就會顯示數據集中數值數據的一些資訊和統計數據。

So we can see here the height of the roller coasters.

是以，我們可以在這裡看到雲霄飛車的高度。

We have a count of 171 values, a mean value of 101 feet, and all this information that gives us a good understanding for the data before we dive into it any further.

我們統計了 171 個數值，平均值為 101 英尺，所有這些資訊都讓我們在深入研究之前對數據有了很好的瞭解。

Okay, so now it's time to move on to the second step, which is data preparation.

好了，現在該進入第二步了，即數據準備。

We have a general understanding of the columns in our data and how many rows we have, but we want to do some cleaning before we actually get into analysis.

我們對數據的列數和行數有了大致的瞭解，但我們希望在進行實際分析之前做一些清理工作。

And it's very important that we first drop any of the columns or rows that we don't want to keep before we continue on, or else we might waste time cleaning up columns that we won't end up using.

重要的是，在繼續之前，我們要先刪除不想保留的列或行，否則可能會浪費時間清理最終用不到的列。

So let's run a df.head again on our data set to remember what sort of columns we have.

是以，讓我們再次在數據集上運行 df.head，以記住我們有哪些列。

One thing to note about this data set is we actually have some columns that have values that are string versions, like speed and length.

關於這個數據集，需要注意的一點是，我們實際上有一些列的值是字符串版本，比如速度和長度。

And we also have similar columns that I have created when I created this data set over to the right here that have the numeric version of those.

我在創建這個數據集時，也在右側創建了類似的列，其中包含這些列的數字版本。

So it's stripped out all the text of mile per hour and converted everything into the same unit here, which is miles per hour.

是以，它剔除了所有 "英里/小時 "的文字，將所有內容轉換成相同的組織、部門，即 "英里/小時"。

So what we're going to want to do is we're going to want to subset this data set just to the columns we want to keep.

是以，我們要做的就是將這個數據集子集為我們想要保留的列。

And there are two ways that I like to do this.

我有兩種方法可以做到這一點。

So if we run df.columns again, we can pull in all the columns and we can actually just subset by copying this list of columns here.

是以，如果我們再次運行 df.columns，就可以調出所有列，實際上，我們可以通過複製這裡的列列表來進行子集。

And then with two brackets, we put in the list of columns, and that will show us all of the columns again.

然後用兩個括號輸入列列表，這樣就能再次顯示所有列。

But now we can start removing column by column what we've decided we don't want to keep.

但現在，我們可以開始一欄一欄地刪除我們決定不保留的內容。

So I already know I want to keep the roller coaster name.

所以我已經知道我想保留雲霄飛車的名字。

So I'm just commenting out the lines in this list that we don't want to keep.

是以，我只是在這份清單中註釋出我們不想保留的行文。

And this makes it easy to keep track of what we've actually dropped.

這樣我們就能很容易地追蹤到我們到底丟了什麼。

Height restriction, I don't think we want to keep.

高度限制，我認為我們不想保留。

Inversions, cost, trains, park section, all of these things may be interesting to look at later.

反轉、成本、火車、公園部分，所有這些都可能是日後研究的有趣內容。

But at this time, we're not going to take a look at them.

But we do want to keep some of this information at the end.

但我們確實想在最後保留一些資訊。

So year introduced, latitude, longitude, I think we want to keep the type of material used here.

是以，在介紹年份、經緯度時，我認為我們要保留這裡使用的材料類型。

All right, so here for speed, I think we just want to keep the miles per hour.

好了，關於速度，我想我們只想保留每小時英里數。

For the height, we just want to keep the value in feet.

對於高度，我們只想保留以英尺為組織、部門的數值。

And yeah, this looks like a good subset of the data that we want to keep.

是的，這看起來是我們想要保留的數據的一個很好的子集。

Actually, let's add in location and status.

其實，我們還可以加上位置和狀態。

Now, if I run this cell, I see that we actually have a smaller data set.

現在，如果我運行這個單元格，就會發現我們的數據集實際上變小了。

Our columns have been reduced just to the columns that we want to keep.

我們的欄目已經減少到只保留我們想要保留的欄目。

We have some information though we will be able to work with.

雖然我們掌握了一些資訊，但我們還是可以利用的。

Now, there's a second way we can deal with subsetting our data sets columns, and that's by using the drop command.

現在，我們還有第二種方法來處理數據集列的子集，那就是使用下拉命令。

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使用 Pandas Python 進行探索性數據分析 (Exploratory Data Analysis with Pandas Python)

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