Python中的pandas表格模塊、文件模塊和數據庫模塊
pandas官方文檔：https://pandas.pydata.org/pandas-docs/stable/?v=20190307135750
pandas基於Numpy，可以看成是處理文本或者表格數據。
pandas中有兩個主要的數據結構，其中Series數據結構類似於Numpy中的一維數組，DataFrame類似於多維表格數據結構。
pandas是python數據分析的核心模塊。它主要提供瞭五大功能:
- 支持文件存取操作，支持數據庫(sql)、html、json、pickle、csv(txt、excel)、sas、stata、hdf等。
- 支持增刪改查、切片、高階函數、分組聚合等單表操作，以及和dict、list的互相轉換。
- 支持多表拼接合並操作。
- 支持簡單的繪圖操作。
- 支持簡單的統計分析操作。
一、Series數據結構
Series是一種類似於一維數組的對象，由一組數據和一組與之相關的數據標簽（索引）組成。
Series比較像列表（數組）和字典的結合體
import numpy as np import pandas as pd df = pd.Series(0, index=['a', 'b', 'c', 'd']) print(df) # a 0 # b 0 # c 0 # d 0 # dtype: int64 print(df.values) # 值 # [0 0 0 0] print(df.index) # 索引 # Index(['a', 'b', 'c', 'd'], dtype='object')
1、Series的創建
import numpy as np
import pandas as pd
df = pd.Series(np.array([1, 2, 3, 4, np.nan]), index=['a', 'b', 'c', 'd', 'e']) # 1、從ndarray創建Series
print(df)
# a 1.0
# b 2.0
# c 3.0
# d 4.0
# e NaN
# dtype: float64
df = pd.Series({'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': np.nan}) # 2、也可以從字典創建Series
dates = pd.date_range('20190101', periods=6, freq='M')
print(type(dates)) #
print(dates)
# DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',
# '2019-05-31', '2019-06-30'],
# dtype='datetime64[ns]', freq='M')
df=pd.Series(0,index=dates) # 3、時間序列索引
print(df)
# 2019-01-31 0
# 2019-02-28 0
# 2019-03-31 0
# 2019-04-30 0
# 2019-05-31 0
# 2019-06-30 0
# Freq: M, dtype: int64
產生時間對象數組：date_range參數詳解：
- start:開始時間
- end:結束時間
- periods:時間長度
- freq:時間頻率，默認為'D'，可選H(our),W(eek),B(usiness),S(emi-)M(onth),(min)T(es), S(econd), A(year),…
2、Series屬性
print(df ** 2) # 3、與標量運算
# a 1.0
# b 4.0
# c 9.0
# d 16.0
# e NaN
# dtype: float64
print(df + df) # 4、兩個Series運算
# a 2.0
# b 4.0
# c 6.0
# d 8.0
# e NaN
# dtype: float64
print(df[0] ) # 5、數字索引； 1.0
print(df[[0, 1, 2]]) # 行索引
# a 1.0
# b 2.0
# c 3.0
# dtype: float64
print(df['a'] ) # 6、鍵索引（行標簽） ；1.0
print(df[['b','c']])
print('a' in df) # 7、in運算；True
print(df[0:2] ) # 8、切片
# a 1.0
# b 2.0
# dtype: float64
print(np.sin(df)) # 9、通用函數
# a 0.841471
# b 0.909297
# c 0.141120
# d -0.756802
# e NaN
# dtype: float64
print(df[df > 1] ) # 10、佈爾值過濾
# b 2.0
# c 3.0
# d 4.0
# dtype: float64
2、Series缺失數據處理
df = pd.Series([1, 2, 3, 4, np.nan], index=['a', 'b', 'c', 'd', 'e']) print(df) # a 1.0 # b 2.0 # c 3.0 # d 4.0 # e NaN # dtype: float64 print(df.dropna() ) # 1、過濾掉值為NaN的行 # a 1.0 # b 2.0 # c 3.0 # d 4.0 # dtype: float64 print(df.fillna(5) ) # 2、用指定值填充缺失數據 # a 1.0 # b 2.0 # c 3.0 # d 4.0 # e 5.0 # dtype: float64 print(df.isnull() ) # 3、返回佈爾數組，缺失值對應為True # a False # b False # c False # d False # e True # dtype: bool print(df.notnull() ) # 4、返回佈爾數組，缺失值對應為False # a True # b True # c True # d True # e False # dtype: bool
二、DataFrame數據結構
DataFrame是一個表格型的數據結構，含有一組有序的列。
DataFrame可以被看做是由Series組成的字典，並且共用一個索引。
1、DataFrame的創建
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.zeros((3, 4))) # 創建一個三行四列的DataFrame
print(df1)
# 0 1 2 3
# 0 0.0 0.0 0.0 0.0
# 1 0.0 0.0 0.0 0.0
# 2 0.0 0.0 0.0 0.0
dates = pd.date_range('20190101', periods=6, freq='M')
np.random.seed(1)
arr = 10 * np.random.randn(6, 4)
print(arr)
# [[ 16.24345364 -6.11756414 -5.28171752 -10.72968622]
# [ 8.65407629 -23.01538697 17.44811764 -7.61206901]
# [ 3.19039096 -2.49370375 14.62107937 -20.60140709]
# [ -3.22417204 -3.84054355 11.33769442 -10.99891267]
# [ -1.72428208 -8.77858418 0.42213747 5.82815214]
# [-11.00619177 11.4472371 9.01590721 5.02494339]]
df = pd.DataFrame(arr, index=dates, columns=['c1', 'c2', 'c3', 'c4']) # 自定義index和column
print(df)
# c1 c2 c3 c4
# 2019-01-31 16.243454 -6.117564 -5.281718 -10.729686
# 2019-02-28 8.654076 -23.015387 17.448118 -7.612069
# 2019-03-31 3.190391 -2.493704 14.621079 -20.601407
# 2019-04-30 -3.224172 -3.840544 11.337694 -10.998913
# 2019-05-31 -1.724282 -8.778584 0.422137 5.828152
# 2019-06-30 -11.006192 11.447237 9.015907 5.024943
2、DataFrame屬性
print(df.dtypes) # 1、查看數據類型 # 0 float64 # 1 float64 # 2 float64 # 3 float64 # dtype: object print(df.index) # 2、查看行索引 # DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30', # '2019-05-31', '2019-06-30'], # dtype='datetime64[ns]', freq='M') print(df.columns) # 3、查看各列的標簽 # Index(['c1', 'c2', 'c3', 'c4'], dtype='object') print(df.values) # 4、查看數據框內的數據，也即不含行標簽和列頭的數據 # [[ 16.24345364 -6.11756414 -5.28171752 -10.72968622] # [ 8.65407629 -23.01538697 17.44811764 -7.61206901] # [ 3.19039096 -2.49370375 14.62107937 -20.60140709] # [ -3.22417204 -3.84054355 11.33769442 -10.99891267] # [ -1.72428208 -8.77858418 0.42213747 5.82815214] # [-11.00619177 11.4472371 9.01590721 5.02494339]] print(df.describe()) # 5、查看數據每一列的極值，均值，中位數，隻可用於數值型數據 # c1 c2 c3 c4 # count 6.000000 6.000000 6.000000 6.000000 # mean 2.022213 -5.466424 7.927203 -6.514830 # std 9.580084 11.107772 8.707171 10.227641 # min -11.006192 -23.015387 -5.281718 -20.601407 # 25% -2.849200 -8.113329 2.570580 -10.931606 # 50% 0.733054 -4.979054 10.176801 -9.170878 # 75% 7.288155 -2.830414 13.800233 1.865690 # max 16.243454 11.447237 17.448118 5.828152 print(df.T) # 6、transpose轉置，也可用Ｔ來操作 # 2019-01-31 2019-02-28 2019-03-31 2019-04-30 2019-05-31 2019-06-30 # c1 16.243454 8.654076 3.190391 -3.224172 -1.724282 -11.006192 # c2 -6.117564 -23.015387 -2.493704 -3.840544 -8.778584 11.447237 # c3 -5.281718 17.448118 14.621079 11.337694 0.422137 9.015907 # c4 -10.729686 -7.612069 -20.601407 -10.998913 5.828152 5.024943 print(df.sort_index(axis=0)) # 7、排序，axis=0 可按行標簽排序輸出； 按行標簽][2019-01-01, 2019-01-02...]從大到小排序 # c1 c2 c3 c4 # 2019-01-31 16.243454 -6.117564 -5.281718 -10.729686 # 2019-02-28 8.654076 -23.015387 17.448118 -7.612069 # 2019-03-31 3.190391 -2.493704 14.621079 -20.601407 # 2019-04-30 -3.224172 -3.840544 11.337694 -10.998913 # 2019-05-31 -1.724282 -8.778584 0.422137 5.828152 # 2019-06-30 -11.006192 11.447237 9.015907 5.024943 print(df.sort_index(axis=1)) # 7、排序，axis=1 可按列頭標簽排序輸出；按列標簽[c1, c2, c3, c4從大到小排序 # c1 c2 c3 c4 # 2019-01-31 16.243454 -6.117564 -5.281718 -10.729686 # 2019-02-28 8.654076 -23.015387 17.448118 -7.612069 # 2019-03-31 3.190391 -2.493704 14.621079 -20.601407 # 2019-04-30 -3.224172 -3.840544 11.337694 -10.998913 # 2019-05-31 -1.724282 -8.778584 0.422137 5.828152 # 2019-06-30 -11.006192 11.447237 9.015907 5.024943 print(df.sort_values(by='c2')) # 8、按數據值來排序 ；按c2列的值從大到小排序 # c1 c2 c3 c4 # 2019-02-28 8.654076 -23.015387 17.448118 -7.612069 # 2019-05-31 -1.724282 -8.778584 0.422137 5.828152 # 2019-01-31 16.243454 -6.117564 -5.281718 -10.729686 # 2019-04-30 -3.224172 -3.840544 11.337694 -10.998913 # 2019-03-31 3.190391 -2.493704 14.621079 -20.601407 # 2019-06-30 -11.006192 11.447237 9.015907 5.024943
3、DataFrame取值
print(df['c2']) # 1、 通過columns標簽取值 # 2019-01-31 -6.117564 # 2019-02-28 -23.015387 # 2019-03-31 -2.493704 # 2019-04-30 -3.840544 # 2019-05-31 -8.778584 # 2019-06-30 11.447237 # Freq: M, Name: c2, dtype: float64 print(df[['c2', 'c3']]) # c2 c3 # 2019-01-31 -6.117564 -5.281718 # 2019-02-28 -23.015387 17.448118 # 2019-03-31 -2.493704 14.621079 # 2019-04-30 -3.840544 11.337694 # 2019-05-31 -8.778584 0.422137 # 2019-06-30 11.447237 9.015907 print(df[0:3]) # 2、 通過columns索引取值 # c1 c2 c3 c4 # 2019-01-31 16.243454 -6.117564 -5.281718 -10.729686 # 2019-02-28 8.654076 -23.015387 17.448118 -7.612069 # 2019-03-31 3.190391 -2.493704 14.621079 -20.601407 print(df.loc['20200228':'20200430']) # 3、loc 通過行標簽取值： # c1 c2 c3 c3 # 2020-02-29 8.654076 -23.015387 17.448118 -7.612069 # 2020-03-31 3.190391 -2.493704 14.621079 -20.601407 # 2020-04-30 -3.224172 -3.840544 11.337694 -10.998913 print(df.iloc[1:3]) # 4、iloc 通過行索引選擇數據,取第二行到三行。 # c1 c2 c3 c3 # 2020-02-29 8.654076 -23.015387 17.448118 -7.612069 # 2020-03-31 3.190391 -2.493704 14.621079 -20.601407 print(df.iloc[2, 1]) # 第三行第二列值：-2.493703754774101 print(df.iloc[1:4, 1:4]) # 第 2-4行與第2-4列： # c2 c3 c4 # 2019-02-28 -23.015387 17.448118 -7.612069 # 2019-03-31 -2.493704 14.621079 -20.601407 # 2019-04-30 -3.840544 11.337694 -10.998913 print(df['c3'] > 10) # 5、 使用邏輯判斷取值 # 2020-01-31 False # 2020-02-29 True # 2020-03-31 True # 2020-04-30 True # 2020-05-31 False # 2020-06-30 False # Freq: M, Name: c3, dtype: bool print(df[df['c3'] > 10]) # 5、 使用邏輯判斷取值 # c1 c2 c3 c4 # 2020-02-29 8.654076 -23.015387 17.448118 -7.612069 # 2020-03-31 3.190391 -2.493704 14.621079 -20.601407 # 2020-04-30 -3.224172 -3.840544 11.337694 -10.998913 print(df[(df['c1'] > 0) & (df['c2'] > -8)]) # c1 c2 c3 c4 # 2019-01-31 16.243454 -6.117564 -5.281718 -10.729686 # 2019-03-31 3.190391 -2.493704 14.621079 -20.601407
4、DataFrame值替換
df.iloc[1:3]=5 # 將2-3行的值設為5 print(df) # c1 c2 c3 c4 # 2020-01-31 16.243454 -6.117564 -5.281718 -10.729686 # 2020-02-29 5.000000 5.000000 5.000000 5.000000 # 2020-03-31 5.000000 5.000000 5.000000 5.000000 # 2020-04-30 -3.224172 -3.840544 11.337694 -10.998913 # 2020-05-31 -1.724282 -8.778584 0.422137 5.828152 df.iloc[0:3, 0:2] = 0 # 將1-3行1-2列的值設為0 print(df) # c1 c2 c3 c4 # 2019-01-31 0.000000 0.000000 -5.281718 -10.729686 # 2019-02-28 0.000000 0.000000 17.448118 -7.612069 # 2019-03-31 0.000000 0.000000 14.621079 -20.601407 # 2019-04-30 -3.224172 -3.840544 11.337694 -10.998913 # 2019-05-31 -1.724282 -8.778584 0.422137 5.828152 # 2019-06-30 -11.006192 11.447237 9.015907 5.024943 # 針對行做處理 df[df['c3'] > 10] = 100 # 將C3列的大於10的行數值設為0 print(df) # c1 c2 c3 c4 # 2019-01-31 0.000000 0.000000 -5.281718 -10.729686 # 2019-02-28 100.000000 100.000000 100.000000 100.000000 # 2019-03-31 100.000000 100.000000 100.000000 100.000000 # 2019-04-30 100.000000 100.000000 100.000000 100.000000 # 2019-05-31 -1.724282 -8.778584 0.422137 5.828152 # 2019-06-30 -11.006192 11.447237 9.015907 5.024943 # 針對行做處理 df = df.astype(np.int32) df[df['c3'].isin([100])] = 1000 # 將C3列的等於100的行數值設為1000 print(df) # c1 c2 c3 c4 # 2019-01-31 0 0 -5 -10 # 2019-02-28 1000 1000 1000 1000 # 2019-03-31 1000 1000 1000 1000 # 2019-04-30 1000 1000 1000 1000 # 2019-05-31 -1 -8 0 5 # 2019-06-30 -11 11 9 5
5、處理丟失數據
print(df.isnull()) # c1 c2 c3 c4 # 0 False True False False # 1 False False False False # 2 False False True False # 3 False False False False # 4 False False False False # 5 False False False True # 6 True True True True print(df.isnull().sum()) # 1、通過在isnull()方法後使用sum()方法即可獲得該數據集某個特征含有多少個缺失值 # c1 1 # c2 2 # c3 2 # c4 2 # dtype: int64 print(df.dropna(axis=0)) # 2、axis=0刪除有NaN值的行 # c1 c2 c3 c4 # 1 4.9 3.0 1.4 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 print(df.dropna(axis=1)) # 3、axis=1刪除有NaN值的列 # Empty DataFrame # Columns: [] # Index: [0, 1, 2, 3, 4, 5, 6] print(df.dropna(how='all')) # 4、刪除全為NaN值得行或列 # c1 c2 c3 c4 # 0 5.1 NaN 1.4 0.2 # 1 4.9 3.0 1.4 0.2 # 2 4.7 3.2 NaN 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 # 5 6.9 3.1 4.9 NaN print(df.dropna(thresh=4)) #5、 保留至少有4個非NaN數據的行，刪除行不為4個值的， # c1 c2 c3 c4 # 1 4.9 3.0 1.4 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 print(df.dropna(subset=['c2'])) # 6、刪除c2中有NaN值的行 # c1 c2 c3 c4 # 1 4.9 3.0 1.4 0.2 # 2 4.7 3.2 NaN 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 # 5 6.9 3.1 4.9 NaN print(df.fillna(value=10)) # 7、用指定值填充nan值 # c1 c2 c3 c4 # 0 5.1 10.0 1.4 0.2 # 1 4.9 3.0 1.4 0.2 # 2 4.7 3.2 10.0 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 # 5 6.9 3.1 4.9 10.0 # 6 10.0 10.0 10.0 10.0
6、合並數據
print(df.isnull()) # c1 c2 c3 c4 # 0 False True False False # 1 False False False False # 2 False False True False # 3 False False False False # 4 False False False False # 5 False False False True # 6 True True True True print(df.isnull().sum()) # 1、通過在isnull()方法後使用sum()方法即可獲得該數據集某個特征含有多少個缺失值 # c1 1 # c2 2 # c3 2 # c4 2 # dtype: int64 print(df.dropna(axis=0)) # 2、axis=0刪除有NaN值的行 # c1 c2 c3 c4 # 1 4.9 3.0 1.4 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 print(df.dropna(axis=1)) # 3、axis=1刪除有NaN值的列 # Empty DataFrame # Columns: [] # Index: [0, 1, 2, 3, 4, 5, 6] print(df.dropna(how='all')) # 4、刪除全為NaN值得行或列 # c1 c2 c3 c4 # 0 5.1 NaN 1.4 0.2 # 1 4.9 3.0 1.4 0.2 # 2 4.7 3.2 NaN 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 # 5 6.9 3.1 4.9 NaN print(df.dropna(thresh=4)) #5、 保留至少有4個非NaN數據的行，刪除行不為4個值的， # c1 c2 c3 c4 # 1 4.9 3.0 1.4 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 print(df.dropna(subset=['c2'])) # 6、刪除c2中有NaN值的行 # c1 c2 c3 c4 # 1 4.9 3.0 1.4 0.2 # 2 4.7 3.2 NaN 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 # 5 6.9 3.1 4.9 NaN print(df.fillna(value=10)) # 7、用指定值填充nan值 # c1 c2 c3 c4 # 0 5.1 10.0 1.4 0.2 # 1 4.9 3.0 1.4 0.2 # 2 4.7 3.2 10.0 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 # 5 6.9 3.1 4.9 10.0 # 6 10.0 10.0 10.0 10.0
二、讀取CSV文件
import pandas as pd from io import StringIO test_data = ''' 5.1,,1.4,0.2 4.9,3.0,1.4,0.2 4.7,3.2,,0.2 7.0,3.2,4.7,1.4 6.4,3.2,4.5,1.5 6.9,3.1,4.9, ,,, ''' test_data = StringIO(test_data) df = pd.read_csv(test_data, header=None) df.columns = ['c1', 'c2', 'c3', 'c4'] print(df) # c1 c2 c3 c4 # 0 5.1 NaN 1.4 0.2 # 1 4.9 3.0 1.4 0.2 # 2 4.7 3.2 NaN 0.2 # 3 7.0 3.2 4.7 1.4 # 4 6.4 3.2 4.5 1.5 # 5 6.9 3.1 4.9 NaN # 6 NaN NaN NaN NaN
三、導入導出數據
pandas的讀寫Excel需要依賴xlrd模塊，所以我們需要去安裝一下， 命令：pip install xlrd
使用df = pd.read_excel(filename)讀取文件，使用df.to_excel(filename)保存文件。
1、讀取文件導入數據
df = pd.read_excel(filename)
讀取文件導入數據函數主要參數：
- sep ：指定分隔符，可用正則表達式如'\s+'
- header=None ：指定文件無行名
- name ：指定列名
- index_col ：指定某列作為索引
- skip_row ：指定跳過某些行
- na_values ：指定某些字符串表示缺失值
- parse_dates ：指定某些列是否被解析為日期，佈爾值或列表
2、寫入文件導出數據
df.to_excel(filename)
寫入文件函數的主要參數：
- sep 分隔符
- na_rep 指定缺失值轉換的字符串，默認為空字符串
- header=False 不保存列名
- index=False 不保存行索引
- cols 指定輸出的列，傳入列表
3、實例
import pandas as pd
import numpy as np
df = pd.read_excel("http://pbpython.com/extras/excel-comp-data.xlsx")
print(df.head())
print(len(df.index)) # 行數 （不包含表頭，且一下均如此）
print(df.index.values) # 行索引
print(len(df.columns)) # 列數
print(df.columns.values) # 列索引
data = df.loc[0].values # 表示第0行數據
data = df.loc[[1, 2]].values # 讀取多行數據（這裡是第1行和第2行）
data = df.iloc[:, 1].values # 讀第1列數據
data = df.iloc[:, [1, 2]].values # 讀取多列數據（這裡是第1列和第2列）
data = df.iloc[1, 2] # 讀取指定單元格數據（這裡是第1行第一列數據）
data = df.iloc[[1, 2], [1, 2]].values # 讀取多行多列數據（第1,2行1,2列的數據）
# 任務：輸出滿足成績大於等於90的數據
temp = []
for i in range(len(df.index.values)):
if df.iloc[i, 3] >= 90:
temp.append(df.iloc[i].values)
df2 = pd.DataFrame(data=temp, columns=df.columns.values)
writer = pd.ExcelWriter('out_test.xlsx')# 不寫index會輸出索引
df2.to_excel(writer, 'Sheet', index=False)
writer.save()
四、pandas讀取json文件
import pandas as pd
strtext = '[{"ttery":"min","issue":"20130801-3391","code":"8,4,5,2,9","code1":"297734529","code2":null,"time":1013395466000},\
{"ttery":"min","issue":"20130801-3390","code":"7,8,2,1,2","code1":"298058212","code2":null,"time":1013395406000},\
{"ttery":"min","issue":"20130801-3389","code":"5,9,1,2,9","code1":"298329129","code2":null,"time":1013395346000},\
{"ttery":"min","issue":"20130801-3388","code":"3,8,7,3,3","code1":"298588733","code2":null,"time":1013395286000},\
{"ttery":"min","issue":"20130801-3387","code":"0,8,5,2,7","code1":"298818527","code2":null,"time":1013395226000}]'
df = pd.read_json(strtext, orient='records')
print(df)
# ttery issue code code1 code2 time
# 0 min 20130801-3391 8,4,5,2,9 297734529 NaN 1013395466000
# 1 min 20130801-3390 7,8,2,1,2 298058212 NaN 1013395406000
# 2 min 20130801-3389 5,9,1,2,9 298329129 NaN 1013395346000
# 3 min 20130801-3388 3,8,7,3,3 298588733 NaN 1013395286000
# 4 min 20130801-3387 0,8,5,2,7 298818527 NaN 1013395226000
df = pd.read_json(strtext, orient='records')
df.to_excel('pandas處理json.xlsx', index=False, columns=["ttery", "issue", "code", "code1", "code2", "time"])
orient參數的五種形式
orient是表明預期的json字符串格式。orient的設置有以下五個值：
1.'split' : dict like {index -> [index], columns -> [columns], data -> [values]}
這種就是有索引，有列字段,和數據矩陣構成的json格式。key名稱隻能是index,columns和data。
s = '{"index":[1,2,3],"columns":["a","b"],"data":[[1,3],[2,8],[3,9]]}'
df = pd.read_json(s, orient='split')
print(df)
# a b
# 1 1 3
# 2 2 8
# 3 3 9
2.'records' : list like [{column -> value}, … , {column -> value}]
這種就是成員為字典的列表。如我今天要處理的json數據示例所見。構成是列字段為鍵,值為鍵值,每一個字典成員就構成瞭dataframe的一行數據。
strtext = '[{"ttery":"min","issue":"20130801-3391","code":"8,4,5,2,9","code1":"297734529","code2":null,"time":1013395466000},\
{"ttery":"min","issue":"20130801-3390","code":"7,8,2,1,2","code1":"298058212","code2":null,"time":1013395406000}]'
df = pd.read_json(strtext, orient='records')
print(df)
# ttery issue code code1 code2 time
# # 0 min 20130801-3391 8,4,5,2,9 297734529 NaN 1013395466000
# # 1 min 20130801-3390 7,8,2,1,2 298058212 NaN 1013395406000
3.'index' : dict like {index -> {column -> value}}
以索引為key,以列字段構成的字典為鍵值。如：
s = '{"0":{"a":1,"b":2},"1":{"a":9,"b":11}}'
df = pd.read_json(s, orient='index')
print(df)
# a b
# 0 1 2
# 1 9 11
4.'columns' : dict like {column -> {index -> value}}
這種處理的就是以列為鍵，對應一個值字典的對象。這個字典對象以索引為鍵,以值為鍵值構成的json字符串。如下圖所示:
s = '{"a":{"0":1,"1":9},"b":{"0":2,"1":11}}'
df = pd.read_json(s, orient='columns')
print(df)
# a b
# 0 1 2
# 1 9 11
5.'values' : just the values array。
values這種我們就很常見瞭。就是一個嵌套的列表。裡面的成員也是列表，2層的。
s = '[["a",1],["b",2]]' df = pd.read_json(s, orient='values') print(df) # 0 1 # 0 a 1 # 1 b 2
五、pandas讀取sql語句
import numpy as np
import pandas as pd
import pymysql
def conn(sql):
# 連接到mysql數據庫
conn = pymysql.connect(
host="localhost",
port=3306,
user="root",
passwd="123",
db="db1",
)
try:
data = pd.read_sql(sql, con=conn)
return data
except Exception as e:
print("SQL is not correct!")
finally:
conn.close()
sql = "select * from test1 limit 0, 10" # sql語句
data = conn(sql)
print(data.columns.tolist()) # 查看字段
print(data) # 查看數據
到此這篇關於Python中的pandas表格模塊、文件模塊和數據庫模塊的文章就介紹到這瞭。希望對大傢的學習有所幫助，也希望大傢多多支持WalkonNet。
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