1. 雲端計算

2. Pandas
Python3.5 functions

3. Pandas

4. install pandas
pip install pandas

5. Series import pandas as pd
s1=pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
print(s1)

6. Dataframe import pandas as pd
city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
population = pd.Series([852469, , ])
d1=pd.DataFrame({ 'City name': city_names, 'Population': population })
print(d1)

7. dataframe.describe() import pandas as pd
california_housing_dataframe = pd.read_csv(" sep=",")
california_housing_dataframe.describe()

8. dataframe.head() import pandas as pd
california_housing_dataframe = pd.read_csv(" sep=",")
print(california_housing_dataframe.head())

9. dataframe.hist() import pandas as pd
california_housing_dataframe = pd.read_csv(" sep=",")
california_housing_dataframe.hist('housing_median_age')

10. Accessing Data import pandas as pd
city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
population = pd.Series([852469, , ])
cities=pd.DataFrame({ 'City name': city_names, 'Population': population })
print(type(cities['City name']))
d1=cities['City name']
print(d1)

11. Accessing Data import pandas as pd
city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
population = pd.Series([852469, , ])
cities=pd.DataFrame({ 'City name': city_names, 'Population': population })
print(type(cities['City name'][1]))
print(cities['City name'][1])
print()
print(type(cities[0:2]))
print(cities[0:2])

12. Manipulating Data import pandas as pd import numpy as np
city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
population = pd.Series([852469, , ])
cities = pd.DataFrame({ 'City name': city_names, 'Population': population })
print("population: ")
print(population)
print()
print(population / 1000.)
p1=np.log(population)
print(p1)
p2=population.apply(lambda val: val > )
print(p2)

13. Manipulating Data print(cities) print()
cities['Area square miles'] = pd.Series([46.87, , 97.92])
cities['Population density'] = cities['Population'] / cities['Area square miles']
pd.options.display.max_columns=5

14. 驗收
Adding a new boolean column that is True if the city has an area greater than 50 square miles.

15. 驗收
import pandas as pd
city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
population = pd.Series([852469, , ])
cities = pd.DataFrame({ 'City name': city_names, 'Population': population })
cities['Area square miles'] = pd.Series([46.87, , 97.92])
cities['Population density'] = cities['Population'] / cities['Area square miles']
pd.options.display.max_columns=5
print(cities)
print()

16. Python3.5 functions

17. 程序 (subroutines or procedures)
擁有特定功能的獨立程式單元

18. 函數 (functions)
程序如果有傳回值，稱為函數

19. 函數 (functions)

20. 函數 (functions)

21. 函數 (functions)
寫一個函數，並定義一個數列的第n項為前2項之和
輸入一個數字m，並印出該數列及數列的的第m項

22. 驗收
輸入一個數字n，將1到n加總後，將結果回傳給主程式並印出。