使用Matplotlib Numpy Pandas构想泰坦尼克号高潮
Did you know, a novel predicted the Titanic sinking 14 years previously to the actual disaster???
您知道嗎，一本小說(shuō)預(yù)言泰坦尼克號(hào)在14年前沉沒(méi)到了真正的災(zāi)難中？？？
In 1898 (14 years before the Titanic sank), American author Morgan Robertson wrote a novel titled ‘The Wreck of the Titan.’
1898年(泰坦尼克號(hào)沉沒(méi)之前的14年)，美國(guó)作家摩根·羅伯森(Morgan Robertson)寫(xiě)了一本名為《泰坦號(hào)的殘骸》的小說(shuō)。
The book was about a fictional ocean liner that sinks due to a collision with an iceberg. In the book, the ship is described as being “unsinkable” and doesn’t have enough lifeboats for everyone on board, sounds familiar yeah you’re right it’s the epic story of titanic which was predicted years ago.
這本書(shū)是關(guān)于一個(gè)虛構(gòu)的遠(yuǎn)洋客輪，由于與冰山相撞而沉沒(méi)。 在這本書(shū)中，這艘船被描述為“不沉”，船上沒(méi)有足夠的救生艇供所有人使用，聽(tīng)起來(lái)不錯(cuò)，是的，這是多年前預(yù)言的泰坦尼克號(hào)的史詩(shī)故事。
The Wreck of Titan泰坦殘骸We cannot conclude whether the author had technical proofs for his prediction, but we as responsible Data science enthusiasts can predict the possibilities and outcomes of the disaster using the data set and what not we can even try to envision the various prospects of the climax.
我們無(wú)法斷定作者是否有預(yù)測(cè)的技術(shù)證據(jù)，但是作為負(fù)責(zé)任的數(shù)據(jù)科學(xué)愛(ài)好者，我們可以使用數(shù)據(jù)集來(lái)預(yù)測(cè)災(zāi)難的可能性和后果，而我們甚至不可以嘗試設(shè)想高潮的各種前景。
I am sure that all of us know what happened to Rose and Jack in the movie Titanic. We all wished that the story had a different ending, didn’t we? Let’s try to make our wish come true by recreating the climax of the story by a simple analysis of the story plot,
我確定我們所有人都知道電影《泰坦尼克號(hào)》中羅斯和杰克的事。 我們都希望這個(gè)故事有一個(gè)不同的結(jié)局，不是嗎？ 讓我們通過(guò)對(duì)故事情節(jié)的簡(jiǎn)單分析來(lái)重現(xiàn)故事的高潮，以實(shí)現(xiàn)我們的愿望，
At the end of the analysis we will be creating three climaxes and come to know the answer of three questions:
在分析的最后，我們將創(chuàng)建三個(gè)高潮，并了解三個(gè)問(wèn)題的答案：
? Is there a possibility for jack to be alive and rose’s survival?
?杰克有可能活著并生存下來(lái)嗎？
? Was there a chance for Jack and Rose together to narrate their adventurous story to their grandchildren?
?杰克和羅斯有沒(méi)有機(jī)會(huì)向?qū)O子們講述他們的冒險(xiǎn)故事？
? Did Cal Hockley (Rose’s Fiancé) have a higher chance of survival as he belonged to the upper-class or what would make the villain dead?
?卡爾·霍克利(羅斯的未婚妻)是屬于上流社會(huì)的人，他有更高的生存機(jī)會(huì)嗎？或者會(huì)使小人喪生？
We are carrying out our analysis using the ‘Matplotlib’, ‘Numpy’,’Pandas’, and ‘Seaborn’ Libraries.
我們正在使用“ Matplotlib”，“ Numpy”，“ Pandas”和“ Seaborn”圖書(shū)館進(jìn)行分析。
Let us see what each library function is:
讓我們看看每個(gè)庫(kù)函數(shù)是什么：
Matplotlib is a python library used for visualizing data sets using various plots; it has more than 50 plots to name a few, bar plot, line plot, histogram, etc.
Matplotlib是一個(gè)python庫(kù)，用于使用各種圖表可視化數(shù)據(jù)集。 它有50多個(gè)圖，例如條形圖，線形圖，直方圖等。
Numpy is also a Python library that provides a high-performance multidimensional array and basic tools to compute with and manipulate these arrays.
Numpy還是一個(gè)Python庫(kù)，它提供了高性能的多維數(shù)組和基本工具，可以使用這些數(shù)組進(jìn)行計(jì)算和操作。
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
Pandas是為Python編程語(yǔ)言編寫(xiě)的用于數(shù)據(jù)處理和分析的軟件庫(kù)。 特別是，它提供了用于處理數(shù)字表和時(shí)間序列的數(shù)據(jù)結(jié)構(gòu)和操作。
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Seaborn是基于Matplotlib的Python數(shù)據(jù)可視化庫(kù)。 它提供了一個(gè)高級(jí)界面，用于繪制引人入勝且內(nèi)容豐富的統(tǒng)計(jì)圖形。
Let us start our journey…
讓我們開(kāi)始我們的旅程...
數(shù)據(jù)探索： (Data Exploration:)
import pandas as pdimport numpy as np
import random as rnd
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
We import all the necessary libraries and read the data set which has the stat of titanic disaster using the ‘pandas’ library.
我們導(dǎo)入所有必需的庫(kù)，并使用“ pandas”庫(kù)讀取具有泰坦尼克號(hào)災(zāi)難狀態(tài)的數(shù)據(jù)集。
titanic_df = pd.read_csv('titanic.csv')Then we display the first five entries using the head command to get a glimpse of the nature of the data and the categories of labels which we are going to explore,
然后，我們使用head命令顯示前五個(gè)條目，以大致了解數(shù)據(jù)的性質(zhì)以及我們將要探索的標(biāo)簽類別，
titanic_df.head()Titanic.csv First 5 entriesTitanic.csv前5個(gè)條目功能分析： (Feature Analysis:)
Looking at titanic_df.describe() we gain a lot of useful insights and find the categorical labels which we can ignore
查看titanic_df.describe()，我們獲得了許多有用的見(jiàn)解，并找到了我們可以忽略的分類標(biāo)簽
titanic_df.describe()Titanic.csv decribe command to see the column characteristicsTitanic.csv decribe命令以查看列特征? PassengerId: Unique for each passenger so this has no relation with the survival label hence this need not be considered for analyzing
?PassengerId：對(duì)于每個(gè)乘客而言都是唯一的，因此它與生存標(biāo)簽沒(méi)有關(guān)系，因此在分析時(shí)無(wú)需考慮這一點(diǎn)
? Survived: Survival is a binary option, 0 for the passenger is dead and 1 for the passenger is alive, so this will be only ‘Y’ variable in XY plotting
?Survived(生存)：Survival(生存)是一個(gè)二進(jìn)制選項(xiàng)，0表示乘客已死亡，1表示該乘客還活著，因此在XY繪圖中這僅是“ Y”變量
? Pclass: Integer equal to 1, 2, or 3 indicating the class of each passenger (lower, middle, or upper), this can be taken for analyzing as this has three inner categories which may contribute to the survival of passengers
?Pclass：等于1、2或3的整數(shù)，表示每個(gè)乘客的等級(jí)(下層，中層或上層)，這可以用于分析，因?yàn)樗哂腥齻€(gè)內(nèi)部類別，可能有助于乘客的生存
? Age: Number representing the age of each passenger, though as we can see in titanic_df.tail(), some passengers have NaN for their age, this can also be considered as maybe younger ones can act swiftly and escape so this can also contribute to the survival label
?年齡：代表每位乘客的年齡的數(shù)字，盡管正如我們?cè)趖itanic_df.tail()中看到的那樣，有些乘客的年齡為NaN，這也可以被認(rèn)為是年齡較小的乘客可以Swift行動(dòng)并逃脫，因此這也可以有所作為到生存標(biāo)簽
? SibSp: Number of siblings also on board, we may not completely ignore this, as it may or may not support the survival label
?SibSp：船上也有兄弟姐妹，我們可能不會(huì)完全忽略這一點(diǎn)，因?yàn)樗赡苤С只蚩赡懿恢С稚鏄?biāo)簽
? Parch: Number of children also on board, this also has a similar case of SibSp
?Parch：船上也有兒童人數(shù)，這也與SibSp類似
? Fare: amount paid for the ticket by each passenger, this may add essence to the Passenger Class label as the higher the fare higher the class of ticket.
?票價(jià)：每位乘客支付的機(jī)票金額，這可能會(huì)在“乘客艙位”標(biāo)簽上添加實(shí)質(zhì)內(nèi)容，因?yàn)槠眱r(jià)越高，機(jī)票艙位越高。
For a quick comparison, we’ll create use NumPy functions to verify the mean, standard deviation, min, and max of numerical columns.
為了進(jìn)行快速比較，我們將創(chuàng)建使用NumPy函數(shù)來(lái)驗(yàn)證數(shù)字列的均值，標(biāo)準(zhǔn)差，最小值和最大值。
columns = list(titanic_df[['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']])def describe_data(data, col):print ('\n\n', col)
print ('_' * 40)
print ('Mean:', np.mean(data)) #NumPy Mean
print ('STD:', np.std(data)) #NumPy STD
print ('Min:', np.min(data)) #NumPy Min
print ('Max:', np.max(data)) #NumPy Maxfor c in columns:
describe_data(titanic_df[c], c)Mean, Standard Deviation, Minimum and Maximum values for each Label每個(gè)標(biāo)簽的平均值，標(biāo)準(zhǔn)偏差，最小值和最大值
這些見(jiàn)解包括： (Insights from these are:)
? Survived is a categorical label with 0 or 1 values.
?Survived是具有0或1個(gè)值的分類標(biāo)簽。
? Around 38% of samples survived representative of the actual survival rate at 32%.
?大約有38％的樣本存活下來(lái)，代表了32％的實(shí)際存活率。
? Most passengers (> 75%) did not travel with parents or children.
?大多數(shù)乘客(> 75％)沒(méi)有和父母或孩子一起旅行。
? Nearly 30% of the passengers had siblings and/or spouse aboard.
?近30％的乘客有兄弟姐妹和/或配偶上車。
? Fares varied significantly with few passengers (<1%) paying as high as $512.
?票價(jià)差異很大，很少乘客(<1％)支付的費(fèi)用高達(dá)512美元。
? Few elderly passengers (<1%) within the age range 65–80.
?65-80歲年齡段的老年乘客(<1％)很少。
Great numbers, Let us move on to realize our dream climaxes…
偉大的數(shù)字，讓我們繼續(xù)前進(jìn)，實(shí)現(xiàn)我們的夢(mèng)想高潮…
高潮1：杰克活著玫瑰死了！ (Climax 1: Jack Lived Rose Died!!!)
If Jack Survived…如果杰克幸存下來(lái)...The vice versa case where Jack narrated his love story to his grandchildren and Rose sadly died, is there a possibility for this? Let us examine by keeping in mind Jack is a male and he belongs to the lower class, and Rose is of Female gender belonging to Upper Class,
反之亦然，杰克向?qū)O子講述自己的愛(ài)情故事，而羅斯則不幸去世，這有可能嗎？ 讓我們記住杰克是男性，他屬于下層階級(jí)，羅斯是女性，屬于上層階級(jí)，
We first need to break the analysis into several parts. First, we will look at the impact sex had on survival by pivoting the data frame.
我們首先需要將分析分為幾個(gè)部分。 首先，我們將通過(guò)透視數(shù)據(jù)框來(lái)研究性別對(duì)生存的影響。
titanic_df.describe(include=['O'])titanic.csv descriptiontitanic.csv說(shuō)明 titanic_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)Male Female survival calculation男女生存計(jì)算This table shows us the percentage of females that survived and the percentage of males that survived. The female survival rate was 74.2%, and the male survival rate was 18.9%. The huge gap between these numbers is an immediate indication that the female survival rate on the Titanic was significantly higher than the male survival rate, and being a woman in fact increase Rose’s chances of survival.
該表向我們顯示了女性存活率和男性存活率。 女性生存率為74.2％，男性生存率為18.9％。 這些數(shù)字之間的巨大差距立即表明，泰坦尼克號(hào)上的女性成活率明顯高于男性成活率，而成為女性實(shí)際上增加了羅斯的生存機(jī)會(huì)。
titanic_df['AgeRange'] = pd.cut(titanic_df['Age'], 16)# Calculate proportion of surviors for each AgeRangetitanic_df[['AgeRange', 'Survived']].groupby(['AgeRange'], as_index=False).mean().sort_values(by='AgeRange', ascending=True)Age Range for survivors幸存者的年齡范圍 age_sex_hist = sns.FacetGrid(titanic_df, col='Survived', row='Sex', hue='Survived')
age_sex_hist.map(plt.hist, 'Age', bins=20)Age Gender Comparisons年齡性別比較
This breakdown gives us an extremely interesting and informative view of the answer to survival rate for women and children. If we look at the data for children 5 and under, we can see that sex didn’t have much of an impact on survival. But in large, most of the children in this sample survived, including the males. Another interesting insight we can see is just how many of the males on board died (aside from male children under 5). Men were more likely to have died than to have survived. When we make the same comparison for females, you can see that females in almost every age range were more likely to survive than to have died. To validate the inferences made here, we can look at the numbers in a table once again, though it becomes harder to read with more variables. However, viewing this type of table can emphasize how helpful histograms can be for visualizing data.
這種分類為我們提供了關(guān)于婦女和兒童生存率答案的極其有趣和有益的見(jiàn)解。 如果我們查看5歲及以下兒童的數(shù)據(jù)，可以發(fā)現(xiàn)性別對(duì)生存沒(méi)有太大影響。 但總體而言，此樣本中的大多數(shù)兒童(包括男性)都得以幸存。 我們可以看到的另一個(gè)有趣的見(jiàn)解是，機(jī)上有多少男性死亡(5歲以下的男性兒童除外)。 男人比死者更有可能死。 當(dāng)我們對(duì)女性進(jìn)行相同的比較時(shí)，您可以看到幾乎每個(gè)年齡段的女性生存的可能性都比死亡的可能性大。 為了驗(yàn)證此處所做的推論，我們可以再次查看表中的數(shù)字，盡管使用更多的變量將更難以閱讀。 但是，查看這種類型的表可以強(qiáng)調(diào)直方圖對(duì)可視化數(shù)據(jù)有多大幫助。
titanic_df[['Sex', 'AgeRange', 'Survived']].groupby(['Sex', 'AgeRange'], as_index=False).mean().sort_values(by='AgeRange', ascending=True)Gender Age Range and Survival Comparisons性別年齡范圍和生存比較Based on these observations and numbers, we can conclude that both women and children had a higher chance of survival and hence our Climax 1 has a lesser probability of realization also we could not change factors like gender and age to make it as per our wish, So there is lesser possibility of Rose dead and Jack Alive, Poor Jack!!!
根據(jù)這些觀察和數(shù)字，我們可以得出結(jié)論，婦女和兒童都有較高的生存機(jī)會(huì)，因此我們的Climax 1的實(shí)現(xiàn)可能性較小，我們也無(wú)法根據(jù)自己的意愿改變性別和年齡等因素，因此，羅斯死亡和杰克活著，可憐的杰克的可能性較小！！！
Poor Jack…可憐的杰克...高潮2：杰克和羅斯逃脫并過(guò)著幸福的生活！ (Climax 2: Jack and Rose Escaped and lived happily!!!)
Imagine while rose was about finish her adventurous trip story, jack joins her and ends as, “that’s how your grandma and grandpa fell in love with each other and ended up together”
想象一下，當(dāng)玫瑰即將結(jié)束她的冒險(xiǎn)旅行故事時(shí)，杰克加入她并以“那是你奶奶和爺爺彼此相愛(ài)并最終在一起的方式”結(jié)尾
That’s the heart-warming climax one would ever want, so what all ways this could be realized, let us analyze the Passenger Class as it is one of the important labels in the titanic data set.
那將是一個(gè)令人心動(dòng)的高潮，因此可以用什么方法實(shí)現(xiàn)，讓我們分析一下旅客等級(jí)，因?yàn)樗翘┨鼓峥颂?hào)數(shù)據(jù)集中的重要標(biāo)簽之一。
Were upper-class passengers more likely to have made it onto a lifeboat than middle and lower class passengers? Let us make it interesting by examining using bar and point plots.
上層階級(jí)的乘客比中層和下層階級(jí)的乘客更有可能登上救生艇嗎？ 讓我們通過(guò)使用條形圖和點(diǎn)圖來(lái)使其有趣。
titanic_df['Passenger'] = 'Passenger'# Create Class column with string values for class
titanic_df['Class'] = titanic_df['Pclass'].map( {1: 'Upper', 2: 'Middle', 3: 'Lower'} )# Create PointPlot for Passengers by Class
bp = sns.pointplot(x='Passenger', y='Survived', hue='Class', data=titanic_df, hue_order=['Lower', 'Middle', 'Upper'])
bp.set(ylabel='% Survivors', xlabel='Passenger Class')Point Plot點(diǎn)圖
This plot shows the average survival and confidence interval of passengers by class. Looking at the breakdown of average survival rates by class shows a correlation between class and rate of survival. Lower class passenger survival ranged somewhere between 20–30%, while upper-class survival ranged somewhere between 55–70%, with middle class ranging somewhere between 35–55%.
該圖顯示了按等級(jí)劃分的旅客的平均生存和置信區(qū)間。 按類別查看平均存活率的細(xì)目分類顯示類別與存活率之間的相關(guān)性。 下層階級(jí)的乘客生存率介于20％至30％之間，而上層階級(jí)的生存率介于55％至70％之間，中層階級(jí)的生存率介于35％至55％之間。
So to make our Climax 2, come true what rose could have done is maybe instead of her joining Jack for the party in his lower passenger class, she could have taken Jack along with her to Upper Class and enjoyed the day, which may have kept them alive for a happy ending.
因此，要使我們的Climax 2成為現(xiàn)實(shí)，玫瑰所能做的可能不是代替她參加低級(jí)客艙的杰克派對(duì)，而是可以將杰克和她一起帶到上層客艙并享受這一天，這可能使他們?yōu)榱艘粋€(gè)幸福的結(jié)局而活著。
Jack and Rose杰克和羅斯高潮3：卡爾·霍克利，想讓他死還是活？？？ (Climax 3: Cal Hockley, want him dead or alive???)
Cal Hockley, the villain who took advantage of Rose’s state and tricked her into his marriage proposal, also seemed to be escaped from the sink what could be the reason?
惡棍卡爾·霍克利(Cal Hockley)趁著羅斯(Rose)的州，騙她參加了求婚，似乎也從水槽中逃了出來(lái)，這可能是什么原因？
Let us explore in detail the passenger class and gender labels as they contribute more significantly and are varying parameters between Jack and Cal using seaborn plots
讓我們?cè)敿?xì)研究旅客等級(jí)和性別標(biāo)簽，因?yàn)樗鼈兊呢暙I(xiàn)更大，并且使用海洋圖在杰克和卡爾之間改變參數(shù)
bps = sns.barplot(x='Sex', y='Survived', hue='Class', data=titanic_df, hue_order=['Lower', 'Middle', 'Upper'])bps.set(ylabel='% Survivors', xlabel='Passenger Sex by Class')Bar Plot條形圖
We can see that middle-class female passengers had almost the same rate of survival as upper-class females, but middle-class men had about the same rate of survival as lower classmen, which further illustrates the greater likelihood of women to have survived. Overall we can observe that upper-class passengers did indeed have a higher chance of survival than lower-class passengers regardless of sex.
我們可以看到，中產(chǎn)階級(jí)女性乘客的生存率與上層階級(jí)女性幾乎相同，但是中產(chǎn)階級(jí)男性的生存率與下層階級(jí)的男性幾乎相同，這進(jìn)一步說(shuō)明了女性生存的可能性更大。 總的來(lái)說(shuō)，我們可以觀察到，不論性別，上層旅客確實(shí)比下層旅客有更高的生存機(jī)會(huì)。
np.corrcoef(x=titanic_df['Pclass'], y=titanic_df['Survived'])Correlation Matrix相關(guān)矩陣A negative correlation tells us is that when class increases (1 → 2 → 3), survival decreases. So, since the lower class is represented as 3, the lower class is correlated with lower survival.
負(fù)相關(guān)告訴我們，當(dāng)階級(jí)增加時(shí)(1→2→3)，生存率降低。 因此，由于較低的類別表示為3，因此較低的類別與較低的生存率相關(guān)。
Unfortunately, there are more chances for the survival of Cal Hockley!
不幸的是，卡爾·霍克利(Cal Hockley)有更多的生存機(jī)會(huì)！
Cal Hockley卡爾·霍克利除了我們對(duì)Climax的假設(shè)外，還有一些限制： (Apart from our assumptions to the Climax, there are certain limitations:)
- As some of these inferences were drawn based on correlation, it’s always important to remember that correlation does not imply causation (relationship). 由于其中一些推論是根據(jù)相關(guān)性得出的，因此記住相關(guān)性并不意味著因果關(guān)系(關(guān)系)總是很重要的。
- Since we know that some passengers did not have a recorded age, entries with ‘NaN’ (null) were not taken into account when running these numbers. 由于我們知道有些乘客沒(méi)有年齡記錄，因此運(yùn)行這些數(shù)字時(shí)不會(huì)考慮帶有“ NaN”(空)的條目。
- Conclusions were drawn based on descriptive statistics, charts, and opted not to run t-tests on the sample. 根據(jù)描述性統(tǒng)計(jì)數(shù)據(jù)，圖表得出結(jié)論，并選擇不對(duì)樣本進(jìn)行t檢驗(yàn)。
有趣的發(fā)現(xiàn)： (Interesting Findings:)
What proportion of passengers in the sample survived?
樣本中有多少乘客幸存下來(lái)？
- 38% of total passengers in the sample survived 樣本中38％的旅客幸存下來(lái)
Did women and children have a higher survival rate?
婦女和兒童的生存率更高嗎？
- The female survival rate in this sample was 55.3% higher than the survival rate for males. 該樣本中女性的生存率比男性的生存率高55.3％。
- Women had a much higher rate of survival than men. 女人的生存率比男人高得多。
- Children under the age of 5, regardless of sex, had a much higher rate of survival 5歲以下的兒童(不論性別)的存活率要高得多
Did upper-class passengers in the sample have an advantage that translated into a higher survival rate than lower-class passengers?
樣本中的上等乘客是否具有比下等乘客具有更高的生存率的優(yōu)勢(shì)？
- The class has a strong correlation with survival, with upper-class passengers having a much larger rate of survival than lower-class passengers, regardless of sex and age. 班級(jí)與生存能力密切相關(guān)，不論性別和年齡，上層旅客的生存率都比下層旅客高得多。
- Upper-class passengers were more likely to survive than lower-class passengers. 上層旅客比下層旅客更有可能生存。
So, as we are approaching the climax of our post, quickly let’s summarize, we got some insights about Matplotlib, Numpy, pandas, and seaborn libraries which are essential and inevitable for data science.
因此，隨著我們接近帖子的高潮，我們快速總結(jié)一下，我們對(duì)Matplotlib，Numpy，pandas和seaborn庫(kù)有了一些見(jiàn)解，這對(duì)于數(shù)據(jù)科學(xué)是必不可少的。
Also instead of mourning on the loss of Jack and the separation of true love, we tried the possibilities to change the climax, what’s exactly the duty data scientist, to analyze the data and come up with useful possibilities to attain desired outcomes.
此外，我們沒(méi)有為杰克的失落和真愛(ài)的分離而哀悼，我們嘗試了改變高潮的可能性，這正是數(shù)據(jù)科學(xué)家的職責(zé)，它是對(duì)數(shù)據(jù)進(jìn)行分析并提出有用的可能性來(lái)達(dá)到預(yù)期效果的方法。
Now, it’s your turn pals to create your own customized climaxes and conclusions with these kinds of simple analysis of the data set and come up with creative and innovative endings of your favorite historical epics, kudos for learners!
現(xiàn)在，輪到您了，您可以通過(guò)對(duì)數(shù)據(jù)集的這種簡(jiǎn)單分析來(lái)創(chuàng)建自己的自定義高潮和結(jié)論，并為您喜歡的歷史史詩(shī)提供創(chuàng)造性的和創(chuàng)新的結(jié)局，對(duì)學(xué)習(xí)者們表示敬意！
The End (Happy Ending)!
結(jié)局(幸福結(jié)局)！
You can find the code and dataset at:
您可以在以下位置找到代碼和數(shù)據(jù)集：
https://github.com/PradeepaK1/Envision-the-Titatnic-Climax-with-Matplotlib-Numpy-Pandas
https://github.com/PradeepaK1/Envision-the-Titatnic-Climax-with-Matplotlib-Numpy-Pandas
Contributors:
貢獻(xiàn)者：
Anjana M P — https://anjana21it.wixsite.com/mysite
Anjana MP- https: //anjana21it.wixsite.com/mysite
Pradeepa K — https://ptljkpd.wixsite.com/pradeepa
Pradeepa K- https: //ptljkpd.wixsite.com/pradeepa
翻譯自: https://medium.com/diva-coders/envision-the-titanic-climax-with-matplotlib-numpy-pandas-d5568cc6d0cc
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