1. df.column.plot(kind=” ”)

• ‘bar’ or ‘barh’ for bar plots ; ‘hist’ for histogram
• ‘box’ for boxplot ; ‘kde’ or ‘density’ for density plots
• ‘area’ for area plots; ‘scatter’ for scatter plots
• ‘hexbin’ for hexagonal bin plots ;‘pie’ for pie plots

2. df.price.hist(bins=10,by=df.room_type)

劃出不同room_type 的價格分布圖

3. df[[“column1”,”column2"]].dropna().plot()

可以將兩個columns 畫一起

seaborn as sns

1. sns.barplot(data=df,x=”price”,y=”room_type”)

根據room type 展現 average price

2. sns.barplot(data=df.loc[df.minimum_nights<7],y=”price”,hue=”room_type”,x=”minimum_nights”)

根據minimum_nights 及不同的 room_type 展現avg price

1. seaborn.distplot — similar to histogram

Gaussian Kernel Density Estimate

sns.distplot(data,hist=False,rug=True,kde_kws={“shade”:True}

2. sns.kdeplot(data, bw=10,shade=Ture)

bw=binwidth

3. sns.regplot(X=”column1",Y=”column2",hue=”column3",data=df)

regplot function generate a scatterplot with a regression line

Pandas data cleaning

1. fill in the missing value:

a. df[‘Test Score’] = df[‘Test Score’].fillna(df[‘Test Score’].interpolate())

interpolate 以遺失值的前後兩者平均數填補遺失值

b. df[“score”]=df[“score”].fillna(df[“score”].mean()) 以平均值填補

c. df=df.fillna(method=”pad”) 以前值填補遺失值

2. non-standard missing value

isnull() function only picks up ‘Nan’ , will not other pick other types of missing values such as a dash(‘-‘) ,blank or even ‘na’

df=df.replace([“-”,” “,”na”],np.nan)

3. creating dataframe from list or scratch

a. df=pd.Dataframe

Months=[“jan”,”feb”,”mar”,”may”]

Days=[1,2,3,5]

df[“month”]=Months

df[“Days”]=Days

4. creating new colnames for dataframe

df.rename(columns={‘id_x’:’purchase_id’, ‘id_y’:’customer_id’,’id’:’product_id’})

5. count 資料轉換是否有錯誤

print(pd.to_datetime(combinedData[‘purch_date’], errors=’coerce’).isnull().value_counts())

print(pd.to_datetime(combinedData['purch_date'], errors='coerce'))

errors=”coerce” …replace error row with np.NAN, if we want to drop the error, we can use dropna afterwards.

errors=”raise” … show the value that error occurs

R coding—

1. numeric(length) → 建立長度為 length 的實數向量

2. names(x) — extract the name of object

3. cumsum function — Returns a vector whose elements are the cumulative sums, products, minima or maxima of the elements of the argument.

cumsum(x) / cumprod(x) / cummax(x) / cummin(x)

cumsum(!is.na(x)) — return the add on T or F of x

x<-c(1, 2, 0, NA, 4, NA, NA, 6)

本文目的: 個人反思進入新領域後的所學所感, 分享MSBA 前三個月課程內容

背景: 大學文組(政治國關畢業輔經濟), 一年美國交換, 三年大陸工作經驗,主要在零售貿易業當國際業務/merchandiser, 因想增強自己的數據分析及hard skills, 決定進修 1 年MSBA 。

MSBA 學程: 一年學程分三個學期完成, 一個學期平均約12 周。 三學分的課程為期12周,1.5 學分的課程則是6周。 T1 課程為5門三學分的課。

1. AI and Big data in bz (三學分|每周3hr課程)

General Level: 7/10 | Growth Level: 7/10

這堂課學習如何應用machine learning algorithms 處理數據, 主要使用工具為googl …

1. complete the kaggle python course

Learn:

1. print(“happy”,end=” ”) the default is \n
2. define a function with docstring to explain
3. turn a string to list with split ; combine words together with join

datestr = '1956-01-31'
year, month, day = datestr.split('-')'/'.join([month, day, year])

4. dicts.items() method lets us iterate over the keys and values of a dictionary simultaneously.

for planet, initial in planet_to_initial.items():
    print("{} begins with \"{}\"".format(planet.rjust(10), initial))

5. enumerate

It allows us to loop over something and have an automatic counter.

my_list = ['apple', 'banana', 'grapes', 'pear']
for c, value in enumerate(my_list, 1):
    print(c, value)

# Output:
# 1…