# F6

## appStore
> Question:
1. Finance
- Installs 前10名 是哪些APP
- 尋找出 Rating 評分5分的 app
2. shopping
- Installs 前10名 是哪些APP
- 尋找出 Rating 評分5分的 app

> Note：

data["Installs"] = pd.to_numeric(data["Installs"].str.replace("[,+]","").replace("Free",""))  #更改格式 
data["Installs"] 

如果無法執行，更改成這樣：

new=[]
for i in range(len(data)):
  new.append(int(data['Installs'][i].replace('+', '').replace(',', '').replace('Free','0')))
data['Installs']=new 

---

#Exam
> 30% 數據分析 (Excel / Python)
> 20% PowerPoint
> 50% 報告＋回答問題
## Statistical studies in baby names
> columns
* 'name','sex','births'
> question
- Baby Name Statistics
1. In 1880, how many male births using the name "Mary"? And how many female births using this name?
2. Calculate the numbers of male and female births in 1880 respectively.
3. 1880's most used names ranking

>> Date Analysis from 1880 to 2010
4. Calculate the numbers of male and female births by year and plot a line chart.
5. Order the names in descending order and show their counts
6. Calculate the most used names in each year between year 1880-2010
7. Plot a line chart. (Count the numbers of Mary and John being used between 1880-2010）
8. Between 1880 and 2010, how many male births and female births using the name "Sam"
9. Calculate how many times "Sam" is being used in each year. (Show it in a Table and plot a line chart)
10. Calculate how many times your name is being used in each year. (Show it in a Table and plot a line chart)

[Python](https://colab.research.google.com/drive/1aGcaoeny8H-vmGHjIuFSQHusmu57Txxe?usp=sharing)

## 澳門不動產交易
> columns
* ['no','group','building','door','volume','price']
* 代號、區域，大廈名稱, 門牌, 成交數量, 實用平方米價, 建築呎價
* 建築呎價＝實用平方米價÷10.76x0.7
> question
1. 澳門實用平方米價最高和最低的區域是哪一個？ （每區-平均(實用平方米價)-找出最高價和最低價）
2. 澳門成交量最高和最低的是哪一個區域？（每區-平均(成交量)-找出最高和最低）
3. 台山區 、 黑沙環及祐漢區 ，氹仔中心區 每年、建築呎價 比較（表格） 和 作圖統計比較分析。
4. 青洲區 、 台山區 ，海洋及小潭山區 每年、建築呎價 比較（表格） 和 作圖統計比較分析。
5. 2017年 - 2022年 每年哪一個大廈名稱，成交最多。（表格）
6. 2017年 - 2022年 每年的成交筆數統計。
7. 建築呎價比較 找出每年 最高和最低的大樓。（表格） 註1
8. 澳門區，建建築呎價 找出每年 最高和最底的大樓。（表格） 註1
9. 氹仔區，建建築呎價 找出每年 最高和最底的大樓。（表格）註1
10. 路環區，建建築呎價 找出每年 最高和最底的大樓。（表格）註1
註1： 每年每幢大樓的建築呎價數據先做平均，再找出每年的最大和最小的大樓。

[Python](https://colab.research.google.com/drive/1s31LlrODUkbqmw-amnEFnQp0jciU580p?usp=sharing)

## BTCUSDT
> question
1. In the BTC Dataset, show the historical highest price and the historical lowest price (Show the values and the respective date). Furthermore, show the highest price and lowest price in each year in a table.
2. Show the average value of the column 'close' of the last 500 records in the BTC dataset.
3. Between 2017-2022, calculate the average price of BTC in each year to form a table and plot a line chart (Use the close column in the Dataset)
4. Between 2018-2022, calculate the average price of BTC in each month to form a table and plot a line chart (Use the close column in the Dataset)
5. Between 2018-2022, calculate the highest price of BTC in each month to form a table and plot a line chart (Use the close column in the Dataset)
6. Between 2018-2022, calculate the difference between the highest price and the lowest price of BTC in each month to form a table and plot a line chart (Use the close column in the Dataset)
7. Bitcoins options is an European-style options, where the last trade day and the last settlement day are both at the last Friday of the expiration month.
8. Calculate how many hours for the price of BTC is greater than 60,000 and between 40,000 and 45,000
9. Calculate the growth rate of BTC price in each year (2018-2019, 2019-2020, 2020-2021, 2021-2022)
10. Use month as the x-axis, value as the y-axis, use close column in the Dataset to to form a table and plot a line chart

[Python](https://colab.research.google.com/drive/1PjO8Z1uNkp7RRisA4lsXWyG6Phrscnwl?usp=sharing)