# Pandas
## pandas
參考資料：https://pandas.pydata.org/pandas-docs/stable/reference/index.html
### Series
#### 製作出一個一維陣列

import pandas as pd
data = pd.Series([1,2,3,4,5])
print(data)
print("Max:",data.max())#output is 5
print("Median",data.median())#output is 3.0
print(data==20)#output is a boolean

#### 觀察Series資料

import pandas as pd
data = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])
print(data.dtype)#查看資料型態
print(data.size)#查看資料數量
print(data.index)#查看資料索引

#### 資料的索引（索引數量要和元素數量一致）

import pandas as pd
data = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])
print(data)

import pandas as pd
data = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])
print(data[2],data[0])##按照順序取得資料
print(data["e"],["d"])##按照索引取得資料

#### Series數學、統計相關函式：<span style = "color:red">`sum()`、`max()`、prod()全部相乘、mean()、median()、std()標準差、nlargest(n)前n大的數字、nsmallest(n)取最小的n個數字</span>

import pandas as pd
data = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])
print(data.max())#最大值
print(data.sum())#總和
print(data.std())#標準差
print(data.median())#中位數
print(data.nlargest(3))#最大的三個數

#### Series字串操作：<span style = "color:green">str.lower()、str.upper()、str.len()、str.cat(sep = "n")用n串在一起、str.contains("n")判斷每一個字串是否包含n、str.replace("n","k")對字串做取代的動作，把n變成k</span>

import pandas as pd
data = pd.Series(['taiwan','台灣','python'])
print(data.str.lower())#全部變小寫
print(data.str.len())#算出每個字串長度
print(data.str.cat(sep = ','))#將字串串在一起，中間以,相接
print(data.str.contains('t'))#判斷字串是否包含t
print(data.str.replace('台灣','中華民國'))#將台灣換成中華民國

### DataFrame
<span style = "color:red">**`列row行column（直行橫列）`**</span>
basis form:DataFrame(dict,index)

import pandas as pd
data = pd.DataFrame({'name':['max','tom','tank'],'salary':[30000,50000,120000]})
print(data)
print('==========')
print(data['name'])#取得行
print('==========')
print(data['salary'])#取得行
print('==========')
print(data.iloc[1])#取得列

#### 資料索引

import pandas as pd
data = pd.DataFrame({'name':['max','tom','steven'],'salary':[30000,80000,120000]},index = ['a','b','c'])
print(data)

#### 觀察資料

import pandas as pd
data = pd.DataFrame({'name':['max','tom','steven'],'salary':[30000,80000,120000]},index = ['a','b','c'])
print(data.size)#資料數量
print(data.shape)#資料形狀
print(data.index)#資料索引

<span style = "color:red">雙維度的資料取得特定的欄或列即可成為單維度的資料即可結合Series一起用</span>
#### 取得列(row)

import pandas as pd
data = pd.DataFrame({'name':['max','tom','steven'],'salary':[30000,80000,120000]},index = ['a','b','c'])
print(data.iloc[1])#按照順序取得第2列
print(data.loc['c'])#按照索引取得第3列

#### 取得行（欄）(column)

import pandas as pd
data = pd.DataFrame({'name':['max','tom','steven'],'salary':[30000,80000,120000]},index = ['a','b','c'])
print(data['name'])
new_data = data['name']
print(new_data.str.upper())
salary_aver = data['salary']
print(salary_aver.mean())

#### 建立新的欄位

import pandas as pd
data = pd.DataFrame({'name':['max','tom','steven'],'salary':[30000,80000,120000]},index = ['a','b','c'])
data['profit'] = [10000,20000,30000]#data[新欄位的名稱] = 列表
data['rank'] = pd.Series([3,6,1],index = ['a','b','c'])#data[新欄位的名稱] = Series的資料
data['cp'] = data['profit']/data['salary']#運用舊欄位產生出新欄位
print(data)

### 資料篩選
#### Series
#### 數字

import pandas as pd
data = pd.Series([30,15,20])
condition = data>18
print(condition)
newData = data[condition]
print(newData)

做出一個比較運算，藉由True/False來判斷此資料是否符合condition。
#### 字串
<span style = "color:red">str.contains</span>

import pandas as pd
data = pd.Series(["你好","python","pandas"])
condition = data.str.contains('p')
newdata = data[condition]
print(newdata)

#### DataFrame

import pandas as pd
data = pd.DataFrame({'name':['max','tom','ken'],'salary':[30000,50000,40000]})
condition = data['salary'] > 30000
print(condition)
newdata = data[condition]
print(newdata)

import pandas as pd
data = pd.DataFrame({'name':['max','tom','ken'],'salary':[30000,50000,40000]})
condition = data['name'] == 'max'
print(condition)
newdata = data[condition]
print(newdata)