python 数据表pandas,python pandas库用法
本文介绍了Python中的pandas表模块、文件模块和数据库模块,并通过示例代码进行了详细介绍。对大家的学习或者工作都有一定的参考价值,有需要的朋友可以参考一下。
00-1010 I、数列数据结构1、数列创建2、数列属性2、数列缺失数据处理2、DataFrame数据结构1、DataFrame创建2、DataFrame属性3、DataFrame值4、DataFrame值替换5、处理丢失数据6、合并数据2、读取CSV文件3、导入数据1、读取文件导入数据2、写入文件导出数据3、实例4、熊猫读取json文件5、熊猫读取sql语句熊猫官方文档:https://pandas.pydata.org/pandas-docs/stable/? v=20190307135755
Pandas基于Numpy,可以看作是处理文本或表格数据。
Pandas有两种主要的数据结构,其中Series数据结构类似于Numpy中的一维数组,DataFrame类似于多维表数据结构。
Pandas是python数据分析的核心模块。它主要提供五个功能3360。
支持文件访问操作,数据库(sql),html,json,pickle,csv(txt,excel),sas,stata,hdf等。它支持添加、删除、搜索、切片、高阶函数、分组和聚合等单表操作,以及dict和list之间的转换。支持多表拼接合并操作。支持简单的绘图操作。支持简单的统计分析操作。
目录
Series是一个类似一维数组的对象,由一组数据和与之相关的一组数据标签(索引)组成。
Series是列表(数组)和字典的组合。
将numpy作为np导入
进口熊猫作为pd
df=pd。Series(0,index=[a , b , c , d])
打印(df)
# a 0
# b 0
# c 0
# d 0
# dtype: int64
打印(df.values) #值
# [0 0 0 0]
打印(df.index) #索引
# Index([a , b , c , d],dtype=object )
一、Series数据结构
将numpy作为np导入
进口熊猫作为pd
Df=pd。Series (np.array ([1,2,3,4,np.nan]),index=[a , b , c , d , e]) # 1。从ndarray创建系列
打印(df)
# a 1.0
# b 2.0
# c 3.0
# d 4.0
# e男
# dtype:浮点64
Df=pd.series ({a :1, b 33602, c 33603, d 33604, e 3360np.nan}) # 2。系列也可以从字典中创建。
date=PD . date _ range( 2019 01 01 ,periods=6,freq=M )
打印(类型(日期))#
打印(日期)
# 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、时间序列索引
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
产生时间对象数组:date_range参数详解:
- start:开始时间
- end:结束时间
- periods:时间长度
- freq:时间频率,默认为'D',可选H(our),W(eek),B(usiness),S(emi-)M(onth),(min)T(es), S(econd), A(year),…
2、Series属性
print(df ** 2) # 3、与标量运算# a 1.0
# b 4.0
# c 9.0
# d 16.0
# e NaN
# dtype: float64
print(df + df) # 4、两个Series运算
# a 2.0
# b 4.0
# c 6.0
# d 8.0
# e NaN
# dtype: float64
print(df[0] ) # 5、数字索引; 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、键索引(行标签) ;1.0
print(df[[b,c]])
print(a in df) # 7、in运算;True
print(df[0:2] ) # 8、切片
# a 1.0
# b 2.0
# dtype: float64
print(np.sin(df)) # 9、通用函数
# a 0.841471
# b 0.909297
# c 0.141120
# d -0.756802
# e NaN
# dtype: float64
print(df[df > 1] ) # 10、布尔值过滤
# b 2.0
# c 3.0
# d 4.0
# dtype: float64
2、Series缺失数据处理
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、过滤掉值为NaN的行
# a 1.0
# b 2.0
# c 3.0
# d 4.0
# dtype: float64
print(df.fillna(5) ) # 2、用指定值填充缺失数据
# a 1.0
# b 2.0
# c 3.0
# d 4.0
# e 5.0
# dtype: float64
print(df.isnull() ) # 3、返回布尔数组,缺失值对应为True
# a False
# b False
# c False
# d False
# e True
# dtype: bool
print(df.notnull() ) # 4、返回布尔数组,缺失值对应为False
# a True
# b True
# c True
# d True
# e False
# dtype: bool
二、DataFrame数据结构
DataFrame是一个表格型的数据结构,含有一组有序的列。
DataFrame可以被看做是由Series组成的字典,并且共用一个索引。
1、DataFrame的创建
import numpy as npimport pandas as pd
df1 = pd.DataFrame(np.zeros((3, 4))) # 创建一个三行四列的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]) # 自定义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属性
print(df.dtypes) # 1、查看数据类型# 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、查看各列的标签
# Index([c1, c2, c3, c4], dtype=object)
print(df.values) # 4、查看数据框内的数据,也即不含行标签和列头的数据
# [[ 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、查看数据每一列的极值,均值,中位数,只可用于数值型数据
# 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转置,也可用T来操作
# 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 可按行标签排序输出; 按行标签][2019-01-01, 2019-01-02...]从大到小排序
# 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 可按列头标签排序输出;按列标签[c1, c2, c3, c4从大到小排序
# 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、按数据值来排序 ;按c2列的值从大到小排序
# 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、 通过columns标签取值# 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、 通过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 通过行标签取值:
# 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 通过行索引选择数据,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行与第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、 使用逻辑判断取值
# 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、 使用逻辑判断取值
# 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值替换
df.iloc[1:3]=5 # 将2-3行的值设为5print(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 # 将1-3行1-2列的值设为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
# 针对行做处理
df[df[c3] > 10] = 100 # 将C3列的大于10的行数值设为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
# 针对行做处理
df = df.astype(np.int32)
df[df[c3].isin([100])] = 1000 # 将C3列的等于100的行数值设为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、处理丢失数据
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、通过在isnull()方法后使用sum()方法即可获得该数据集某个特征含有多少个缺失值
# c1 1
# c2 2
# c3 2
# c4 2
# dtype: int64
print(df.dropna(axis=0)) # 2、axis=0删除有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删除有NaN值的列
# Empty DataFrame
# Columns: []
# Index: [0, 1, 2, 3, 4, 5, 6]
print(df.dropna(how=all)) # 4、删除全为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个非NaN数据的行,删除行不为4个值的,
# 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、删除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、合并数据
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、通过在isnull()方法后使用sum()方法即可获得该数据集某个特征含有多少个缺失值
# c1 1
# c2 2
# c3 2
# c4 2
# dtype: int64
print(df.dropna(axis=0)) # 2、axis=0删除有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删除有NaN值的列
# Empty DataFrame
# Columns: []
# Index: [0, 1, 2, 3, 4, 5, 6]
print(df.dropna(how=all)) # 4、删除全为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个非NaN数据的行,删除行不为4个值的,
# 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、删除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、读取CSV文件
import pandas as pdfrom 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、导入导出数据
pandas的读写Excel需要依赖xlrd模块,所以我们需要去安装一下, 命令:pip install xlrd
使用df = pd.read_excel(filename)读取文件,使用df.to_excel(filename)保存文件。
1、读取文件导入数据
df = pd.read_excel(filename)
读取文件导入数据函数主要参数:
- sep :指定分隔符,可用正则表达式如'\s+'
- header=None :指定文件无行名
- name :指定列名
- index_col :指定某列作为索引
- skip_row :指定跳过某些行
- 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为日期,布尔值或列表
2、写入文件导出数据
df.to_excel(filename)
写入文件函数的主要参数:
- sep 分隔符
- na_rep 指定缺失值转换的字符串,默认为空字符串
- header=False 不保存列名
- index=False 不保存行索引
- cols 指定输出的列,传入列表
3、实例
import pandas as pdimport numpy as np
df = pd.read_excel("http://pbpython.com/extras/excel-comp-data.xlsx")
print(df.head())
print(len(df.index)) # 行数 (不包含表头,且一下均如此)
print(df.index.values) # 行索引
print(len(df.columns)) # 列数
print(df.columns.values) # 列索引
data = df.loc[0].values # 表示第0行数据
data = df.loc[[1, 2]].values # 读取多行数据(这里是第1行和第2行)
data = df.iloc[:, 1].values # 读第1列数据
data = df.iloc[:, [1, 2]].values # 读取多列数据(这里是第1列和第2列)
data = df.iloc[1, 2] # 读取指定单元格数据(这里是第1行第一列数据)
data = df.iloc[[1, 2], [1, 2]].values # 读取多行多列数据(第1,2行1,2列的数据)
# 任务:输出满足成绩大于等于90的数据
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)# 不写index会输出索引
df2.to_excel(writer, Sheet, index=False)
writer.save()
四、pandas读取json文件
import pandas as pdstrtext = [{"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处理json.xlsx, index=False, columns=["ttery", "issue", "code", "code1", "code2", "time"])
orient参数的五种形式
orient是表明预期的json字符串格式。orient的设置有以下五个值:
1.'split' : dict like {index -> [index], columns -> [columns], data -> [values]}
这种就是有索引,有列字段,和数据矩阵构成的json格式。key名称只能是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}]
这种就是成员为字典的列表。如我今天要处理的json数据示例所见。构成是列字段为键,值为键值,每一个字典成员就构成了dataframe的一行数据。
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}}
以索引为key,以列字段构成的字典为键值。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}}
这种处理的就是以列为键,对应一个值字典的对象。这个字典对象以索引为键,以值为键值构成的json字符串。如下图所示:
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这种我们就很常见了。就是一个嵌套的列表。里面的成员也是列表,2层的。
s = [["a",1],["b",2]]df = pd.read_json(s, orient=values)
print(df)
# 0 1
# 0 a 1
# 1 b 2
五、pandas读取sql语句
import numpy as npimport pandas as pd
import pymysql
def conn(sql):
# 连接到mysql数据库
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语句
data = conn(sql)
print(data.columns.tolist()) # 查看字段
print(data) # 查看数据
到此这篇关于Python中的pandas表格模块、文件模块和数据库模块的文章就介绍到这了。希望对大家的学习有所帮助,也希望大家多多支持盛行IT软件开发工作室。
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