Pandas中DataFrame的操作

Pandas有两个核型概念: Series、DataFrame。 Series类似于Python中的list，可以理解为表格中的一列数据。 DataFrame包含Series，理解为一个表格。核心价值就在于基于表格数据的过滤、转换、统计、分析并输出可视化图表。

后面所有的示例都是基于以下几份测试数据:

首页PV	搜索页PV	注册数	下单用户数	订单数
985	290	98	46	40
211	200	21	43	50
688	201	19	68	70
766	228	71	72	80

所有的代码示例都假设你已经导入如下python包:

import pandas as pd
import numpy as np
import matplotlib as plt
import sys
import MySQLdb

pv_conv = {
    '首页PV': [985, 211, 688, 766],
    '搜索页PV': [290, 200, 201, 228],
    '注册数': [98, 21, 19, 71],
    '下单用户数': [46, 43, 68, 72],
    '订单数': [40, 50, 70, 80],
}

1. 创建DataFrame

1.1 基于`dict`创建

基于代码来创建DataFrame的场景还是很常见的，这样数据就可以是来自任意位置、采用任意格式。DataFrame很多操作都是基于index列完成的。

DataFrame的处理本省兼容性很强，天然支持类似2.1.3 基于JSON创建里的orient='split'、orient='records'、orient='index'，orient='columns'

1. 类`orient='split'`格式

我们需要将index、columns、data分成3个参数传递:

index = ["2020-09-01", "2020-09-02", "2020-09-03", "2020-09-04"]
columns = ["首页PV", "搜索页PV", "注册数", "下单用户数", "订单数"]
data = [
    [1, 2, 3, 4, 5],
    [6, 7, 8, 9, 10],
    [11, 12, 13, 14, 15],
    [16, 17, 18, 19, 20]
]

record_json = pd.DataFrame(index=index, columns=columns, data=data)
print(record_json)

2. 类`orient='records'`格式

也能支持使用orient='records'格式的数据:

data = [
    {"日期": "2020-09-01", "首页PV":"1",  "搜索页PV": "1", "注册数" : "1" , "下单用户数" : "1", "订单数" : "1"},
    {"日期": "2020-09-02", "首页PV":"2",  "搜索页PV": "2", "注册数" : "2" , "下单用户数" : "2", "订单数" : "2"},
    {"日期": "2020-09-03", "首页PV":"3",  "搜索页PV": "3", "注册数" : "3" , "下单用户数" : "3", "订单数" : "3"},
    {"日期": "2020-09-04", "首页PV":"4",  "搜索页PV": "4", "注册数" : "4" , "下单用户数" : "4", "订单数" : "4"}
]

record_json = pd.DataFrame(data)
print(record_json)

3. 类`orient='columns'`格式

pv_conv = {
    '首页PV': [985, 211, 688, 766],
    '搜索页PV': [290, 200, 201, 228],
    '注册数': [98, 21, 19, 71],
    '下单用户数': [46, 43, 68, 72],
    '订单数': [40, 50, 70, 80],
}

record_json = pd.DataFrame(pv_conv)
print(record_json)

4. 不支持类`orient='index'`格式

这种格式以数组下标做为key，以行数据做为value，组成一个dict传给DataFrame，DataFrame无法区分orient='columns'。

5. 不支持`orient='values'`格式

这种格式和类orient='split'格式也是冲突的，传入的二维数组会被当成data字段，导致你的index和columns都被当做数据

6. `index`的设定

自动生成index列

适用于index列无实际意义的场景，index默认从0开始，相当于数组的位置索引。

df = pd.DataFrame(pv_conv)

image.png

手动切换index列

df = pd.DataFrame(pv_conv)
df.set_index('日期')

额外提供index列

数据处理的时候，自己将index列单独拿出。

df = pd.DataFrame(pv_conv, index=["2020-09-01", "2020-09-03", "2020-09-03", "2020-09-04"])

image.png

1.2 基于csv文件创建

csv文件内容:

日期,首页PV,搜索页PV,注册数,下单用户数,订单数
2020-09-01,985,290,98,46,40
2020-09-03,211,200,21,43,50
2020-09-03,688,201,19,68,70
2020-09-04,766,228,71,72,80

1. 默认`index`列自动生成

csv = pd.read_csv('d:/pv_conv.csv')
print(csv)

image.png

2. 通过列索引指定`index`

csv = pd.read_csv('d:/pv_conv.csv', index_col=0)
print(csv)

image.png

3. Python 2.7下pandas遇到的问题

csv文件如果使用index_col(默认使用)，会导致列头和实际数据错位一列，需要指定index_col=False才能避免改问题。

csv = pd.read_csv("e:/wht_mobile.csv", index_col=False)

1.3 基于json创建

1. 字符串，格式`split`

可以通过orient指定json格式，如果orient='split'格式, index和columns指定单独字段，data是二维数组，数组里每一个元素对应数据的一行

{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20]
     ]
}

split_json = pd.read_json(tj, orient='split')
print(split_json)

所有JSON格式创建的DataFrame都需要默认生成0开始的类似数组下标的index，如果数据里有index列，需要你自己指定。

image.png

2. 字符串，格式`reacords`

如果orient='records'格式, 数据是json对象的数组，数组里的每一个对象对应一行记录:

[
    {"日期": "2020-09-01", "首页PV":"1",  "搜索页PV": "1", "注册数" : "1" , "下单用户数" : "1", "订单数" : "1"},
    {"日期": "2020-09-02", "首页PV":"2",  "搜索页PV": "2", "注册数" : "2" , "下单用户数" : "2", "订单数" : "2"},
    {"日期": "2020-09-03", "首页PV":"3",  "搜索页PV": "3", "注册数" : "3" , "下单用户数" : "3", "订单数" : "3"},
    {"日期": "2020-09-04", "首页PV":"4",  "搜索页PV": "4", "注册数" : "4" , "下单用户数" : "4", "订单数" : "4"}
]

==这里有个非常坑的点是，数据元素后面的","，在最后一个元素后面不能有，json格式本身也不允许，但是如果你放了提示很诡异，这个问题困扰了我好几个小时==

record_json = pd.read_json(rj, orient='records', encoding="utf-8", dtype={"a": str, "b": str})
print(record_json)

默认是自动生成index列，如果要自己指定，通过set_index修改，这个方法是通用的

image.png

3. 字符串，格式`index`

如果orient='index'格式，数据的json格式会变成object，以位置索引为key

{
    "0":{"日期": "2020-09-01", "首页PV":"1",  "搜索页PV": "1", "注册数" : "1" , "下单用户数" : "1", "订单数" : "1"},
    "1":{"日期": "2020-09-02", "首页PV":"2",  "搜索页PV": "2", "注册数" : "2" , "下单用户数" : "2", "订单数" : "2"},
    "2":{"日期": "2020-09-03", "首页PV":"3",  "搜索页PV": "3", "注册数" : "3" , "下单用户数" : "3", "订单数" : "3"},
    "3":{"日期": "2020-09-04", "首页PV":"4",  "搜索页PV": "4", "注册数" : "4" , "下单用户数" : "4", "订单数" : "4"}
}

record_json = pd.read_json(ridx, orient='index', encoding="utf-8")
print(record_json)

4. 字符串，格式`columns`

columns是导入的默认格式，输入json是一个object，每个key对应一列数据

{
    "首页PV": [985, 211, 688, 766],
    "搜索页PV": [290, 200, 201, 228],
    "注册数": [98, 21, 19, 71],
    "下单用户数": [46, 43, 68, 72],
    "订单数": [40, 50, 70, 80],
    "日期":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"]
}

record_json = pd.read_json(ridx, orient='columns', encoding="utf-8")
print(record_json)

5. 字符串，格式`values`

如果指定格式values，数据是一个json的二维数据，每一个元素代表一行

[
    ["首页PV","搜索页PV","注册数","下单用户数","订单数","日期"],
    ["985","290","98","46","40","2020-09-01"],
    ["211","200","21","43","50","2020-09-02"],
    ["688","201","19","68","70","2020-09-03"],
    ["766","228","71","72","80","2020-09-04"]
]

record_json = pd.read_json(ridx, orient='values', encoding="utf-8")
print(record_json)

6. 从文件创建

文件和字符串的区别仅仅是数据来源问题，同样适用上面提到的orient，默认orient=columns。

json文件内容:

{
    "首页PV": [985, 211, 688, 766],
    "搜索页PV": [290, 200, 201, 228],
    "注册数": [98, 21, 19, 71],
    "下单用户数": [46, 43, 68, 72],
    "订单数": [40, 50, 70, 80],
    "日期":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"]
}

json = pd.read_json("d:/pv_conv.json")
json = json.set_index("日期")
print(json)

按自己的需求指定index列，如果不指定默认是类似数组下标索引，从0开始的数字。

1.4 从数据库表创建

# 通过数据库导入
mysql_conn = MySQLdb.connect(host='192.168.36.92', user='hadoop', passwd='aa.123', port=3306, db='dp', charset="utf8")
db = pd.read_sql_query("select * from client", mysql_conn)
# print(db)

# 通过数据库导入, 指定索引列， 这里用位置索引报错
db = pd.read_sql_query("select * from client", mysql_conn, index_col='id')
# print(db)

# 切换索引列
db = db.set_index("name")
print(db)

2. 数据操作

2.1 基于`index`列的筛选

1. 自动生成`index`列的筛选

df = pd.DataFrame(pv_conv)
row = df.loc[0]

image.png

2. 额外提供`index`列的筛选

df = pd.DataFrame(pv_conv, index=["2020-09-01", "2020-09-03", "2020-09-03", "2020-09-04"])
row = df.loc['2020-09-01']

image.png

3. 基于`index`范围切片

指定index的起始范围，返回包含两端的边界值。

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.loc['2020-09-02':'2020-09-03'])

image.png

2.2 基于行位置索引

1. 根据行位置筛选

操作和数组下标一致


js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.iloc[0])

image.png

2. 基于行位置范围筛选

这里比较诡异的一个点是，loc是包含起始值的，但iloc不包含结束值。

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.iloc[1:3])

image.png

2.3 前N行、后N行

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv)

print(pv_conv.head(2))
print(pv_conv.tail(1))

2.4 数据去重，基于`index`

append是将两个DataFrame拼接，这里我们用pv_conv拼接自己，导致数据重复出现，再使用drop_duplicates去重

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.shape)

pv_conv = pv_conv.append(pv_conv)
print(pv_conv.shape)

print(pv_conv)

# 删除重复的
pv_conv = pv_conv.drop_duplicates()
print(pv_conv)

image.png

通过指定keep参数，控制保留那条数据:

取值	说明
keep=first	保留第一条数据
keep=last	保留最后一条
keep=False	删除所有重复

pv_conv = pv_conv.drop_duplicates(keep=False)
print(pv_conv)

2.5 空值处理

1. 判断是否为null

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.isnull())

image.png

单纯的判断感觉意义不大

2. 空值统计

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.isnull().sum())

image.png

3. 填充空值

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv)

pv_conv = pv_conv.fillna('HI')
print(pv_conv)

image.png

2.6 数据清洗，删除空数据

1. 删除有空值的行

删除有空值的行，只要有一个空值这一行就会被丢弃

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv)
print(pv_conv.dropna())

image.png

2. 删除有空值的列

我们能通过dropna的参数axis指定删除行还是列，默认axis=0表示删除行，指定axis=1表示删除列

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv)
print(pv_conv.dropna(axis=1))

image.png

2.7 基于条件的筛选

1. 过滤条件

通过DataFrame['列名']能拿到Series类型，Series配合条件运算符会生成我们的过滤条件，过滤条件的输出类似于isnull()方法的输出。

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')

condition = (pv_conv['首页PV'] > 6)
print(condition)

image.png

DataFrame[过滤条件]返回的是结果为True的那些行。

2. 逻辑运算符

过滤条件支持逻辑运算符，&表示与，|表示或。每个条件都用()包含，&、|在括号外。

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')

condition = (pv_conv['首页PV'] > 6) & (pv_conv['下单用户数'] < 18)
print(condition)

print(pv_conv[condition])
print(pv_conv[(pv_conv['首页PV'] > 6) & (pv_conv['下单用户数'] < 18)]) # 也可以直接使用

3. 在列表中，类似数据库的`in`

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')

print(pv_conv[pv_conv['首页PV'].isin([1, 11])])

image.png

4. 基于过滤条件的筛选

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv)

home_page_pv = pv_conv[pv_conv['首页PV'] > 10]
print(home_page_pv)

image.png

2.8 转换函数

通过列名获取DataFrame列Series，结合apply方法，提供转换函数，能对特定列数据进行转换。转换结果需要赋值会DataFrame['列名']后才能使修改生效。

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [3, 7, 8, null, 10],
            [5, null, 13, 14, 15],
            [7, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')

def multiply_pv(pv):
    return pv * 100 + random.randint(0, 100)

temp = pv_conv['首页PV'].apply(multiply_pv)
print(temp)

pv_conv['首页PV'] = temp
print(pv_conv)

image.png

3. 反射、DML

3.1 info 获取列名和类型，非空行数

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.info())

image.png

3.2 shape 获取行数和列数

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.shape)

说明当前DataFrame有4行5列

image.png

3.3 获取列名

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.columns)

image.png

3.4 修改列名

1. 修改指定列列名

将DataFrame中的订单数列名改成order_count

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.columns)

pv_conv.rename(columns={
    '订单数': 'oder_count'
}, inplace=True)

print(pv_conv.columns)

2. 批量改名

一次修改多个列名:

pv_conv.columns = ['首页PV', 'search_pv', 'register_count', '下单用户数', '订单数']
print(pv_conv.columns)

3. 基于列表解析，用函数自动改名

适用场景是基于规则修改，比如将列名都改成大写的

pv_conv.columns = [c.upper() for c in temp]
print(pv_conv)

image.png

4. 表操作

4.1 union

将两个DataFrame内容拼接成一个DataFrame

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.shape)

pv_conv = pv_conv.append(pv_conv)
print(pv_conv.shape)

4.2 join

通过DataFrame.join方法我们可以见一个DataFrame和另外的一个到多个Series或DataFrame做关联操作，如果是多个用list传入。

DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)

参数名	说明
other	要关联的`Series`或`DataFrame`，多个用数组传参
on	用来和`DataFrame`关联的`other`上的字段名称，默认是用`index`字段关联
how	关联方式，类型与数据库关联的`left join`、`right join`的概念，可选值是:`left`,`right`, `outer`, `inner`
lsuffix	左侧字段名加前缀，用于避免重名
rsuffix	右侧字段名加前缀，用于避免重名
sort	是否对结果按关联字段排序

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')

js2 = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["APP下载","APP活跃"],
    "data":[
            [111,112],
            [121,122],
            [131,132],
            [141,142]
     ]
}
'''

print(pv_conv)

pv_conv2 = pd.read_json(js2, orient='split')
print(pv_conv2)

pv_conv3 = pv_conv.join(pv_conv2)
print(pv_conv3)

image.png

4.3 差集

DataFrame本身不直接提供方法支持差集，我们是利用drop_duplicate实现的差集，pv_conv拼接两次pv_conv2，这样会导致pv_conv2上的index必然重复，再指定keep=False这样就保证pv_conv2上的说有index只都会删除。

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')

js2 = '''
{
    "index":["2020-09-01","2020-09-02"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10]
     ]
}
'''

pv_conv2 = pd.read_json(js2, orient='split')

pv_diff = pv_conv.append(pv_conv2)
pv_diff = pv_diff.append(pv_conv2)
pv_diff = pv_diff.drop_duplicates(keep=False)

print(pv_diff)

image.png

4.4 统计

1. describe统计: 个数、均值、最大最小值、百分位

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.describe())

image.png

对于非数值类型，返回的就是行数、distinct行数、出现频率最高的关键字、出现的次数

2. value_count: 出现频率统计

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.value_counts())
print(pv_conv['首页PV'].value_counts())

DataFrame.value_count的实际作用待考究。Serries返回每个特定值出现的频率

image.png

3. corr: 计算列之间的相关性

计算各个列之间的相关性，正数表明正相关(一个涨另一个也上涨), 负数表明负相关(一个涨一个跌)，越接近1表示相关性越大

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(pv_conv.corr())

image.png

4. 百分位

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [3, 7, 8, null, 10],
            [5, null, 13, 14, 15],
            [7, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')

print(pv_conv['首页PV'].quantile(0.25))

image.png

5. 平均值

print(csv[csv['用户ID'] < 18000].head(3)['用户ID'].mean())

4.5 列裁剪

关于行裁切可以看一下2.2.1和2.2.2基于index和数组下标的切片。

1. 获取特定列

默认pv_conv['订单数']返回的是Series，如果想要返回DataFrame需要传递一个数组，pv_conv[['订单数','首页PV']

js = '''
{
    "index":["2020-09-01","2020-09-02","2020-09-03","2020-09-04"],
    "columns":["首页PV","搜索页PV","注册数","下单用户数","订单数"],
    "data":[
            [1, 2, 3, 4, 5],
            [6, 7, 8, null, 10],
            [11, null, 13, 14, 15],
            [16, null, 18, 19, 20]
     ]
}
'''

pv_conv = pd.read_json(js, orient='split')
print(type(pv_conv['订单数']))
print(pv_conv['订单数'])

print(type(pv_conv[['订单数', '首页PV']]))
print(pv_conv[['订单数', '首页PV']])

image.png

5. 导出

5.1 to_csv

如果写出有中文，必须指定encoding

db.to_csv('e:/test_csv.csv', encoding='utf-8')

5.2 to_json

可以通过orient指定输出格式，默认是orient='columns'。

db.to_json("e:/test_json.json")

5.3 to_sql

db.to_sql('table_name',mysql_conn)

参考资料

https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/

Pandas中DataFrame的操作

1. 创建DataFrame

1.1 基于dict创建

1. 类orient='split'格式

2. 类orient='records'格式

3. 类orient='columns'格式

4. 不支持类orient='index'格式

5. 不支持orient='values'格式

6. index的设定

1.2 基于csv文件创建

1. 默认index列自动生成

2. 通过列索引指定index

3. Python 2.7下pandas遇到的问题

1.3 基于json创建

1. 字符串，格式split

2. 字符串，格式reacords

3. 字符串，格式index

4. 字符串，格式columns

5. 字符串，格式values

6. 从文件创建

1.4 从数据库表创建

2. 数据操作

2.1 基于index列的筛选

1. 自动生成index列的筛选

2. 额外提供index列的筛选

3. 基于index范围切片

2.2 基于行位置索引

1. 根据行位置筛选

2. 基于行位置范围筛选

2.3 前N行、后N行

2.4 数据去重，基于index

2.5 空值处理

1. 判断是否为null

2. 空值统计

3. 填充空值

2.6 数据清洗，删除空数据

1. 删除有空值的行

2. 删除有空值的列

2.7 基于条件的筛选

1. 过滤条件

2. 逻辑运算符

3. 在列表中，类似数据库的in

4. 基于过滤条件的筛选

2.8 转换函数

3. 反射、DML

3.1 info 获取列名和类型，非空行数

3.2 shape 获取行数和列数

3.3 获取列名

3.4 修改列名

1. 修改指定列列名

2. 批量改名

3. 基于列表解析，用函数自动改名

4. 表操作

4.1 union

4.2 join

4.3 差集

4.4 统计

1. describe统计: 个数、均值、最大最小值、百分位

2. value_count: 出现频率统计

3. corr: 计算列之间的相关性

4. 百分位

5. 平均值

4.5 列裁剪

1. 获取特定列

5. 导出

5.1 to_csv

5.2 to_json

5.3 to_sql

参考资料

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