标签：name 17 python age DataFrame df 数据结构 orient 21

有时候DataFrame，我们不一定要保存成文件、或者入数据库，而是希望保存成其它的格式，比如字典、列表、json等等。当然，读取DataFrame也不一定非要从文件、或者数据库，根据现有的数据生成DataFrame也是可以的，那么该怎么做呢？我们来看一下

一 . DataFrame转成python中的数据格式

1 . 转成json

DataFrame转成json，可以使用df.to_json()方法

import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

print(df.to_json())  
# {"name":{"0":"mashiro","1":"satori","2":"koishi","3":"nagisa"},"age":{"0":17,"1":17,"2":16,"3":21}}

我们看到虽然转化成了json，但是有些不完美，那就是它把索引也算进去了

import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

# 如果不想加索引的话，那么指定index=False即可
try:
    print(df.to_json(index=False))
except Exception as e:
    print(e)  # 'index=False' is only valid when 'orient' is 'split' or 'table'
# 但是它报错了，说如果index=False，那么orient必须指定我split或者table

我们看一下这个orient是什么

首先orient可以有如下取值：split、records、index、columns、values、table

我们分别演示一下，看看orient取不同的值，结果会有什么变化

• orient='split'

import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

print(df.to_json(orient="split"))
"""
{
 "columns":["name","age"],
 "index":[0,1,2,3],
 "data":[["mashiro",17],["satori",17],["koishi",16],["nagisa",21]]
}
"""
print(df.to_json(orient="split", index=False))
"""
{
 "columns":["name","age"],
 "data":[["mashiro",17],["satori",17],["koishi",16],["nagisa",21]]
}
"""

我们看到会变成三个键值对，分别是列名、索引、数据

• orient='records'

import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

print(df.to_json(orient="records"))
"""
[{"name":"mashiro","age":17},
 {"name":"satori","age":17},
 {"name":"koishi","age":16},
 {"name":"nagisa","age":21}]
"""

这种格式的数据是比较常用的，相当于列名和每一行数据组合成一个字典，然后存在一个列表里面。并且我们看到生成json默认跟索引没啥关系，所以不需要、也不可以加index=False

• orient='index'

import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

print(df.to_json(orient="index"))
"""
{
 "0":{"name":"mashiro","age":17},
 "1":{"name":"satori","age":17},
 "2":{"name":"koishi","age":16},
 "3":{"name":"nagisa","age":21}
}
"""

类似于records，只不过这里把字典作为value放在了外层字典里，其中key为对应的索引。当然这里同样不可以加index=False

• orient='columns'

import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

print(df.to_json(orient="columns"))
"""
{"name":{"0":"mashiro","1":"satori","2":"koishi","3":"nagisa"},"age":{"0":17,"1":17,"2":16,"3":21}}
"""

我们看到这个和不指定orient得到结果是一样的，其实不指定的话orient默认是columns

• orient=values

import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

print(df.to_json(orient="values"))
"""
[["mashiro",17],["satori",17],["koishi",16],["nagisa",21]]
"""
# 我们看到当orient指定为values，会只获取数据
# 另外这个方式类似于to_numpy
print(df.to_numpy())
"""
[['mashiro' 17]
 ['satori' 17]
 ['koishi' 16]
 ['nagisa' 21]]
"""
orient=table
import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

# 以数据库二维表的形式返回
print(df.to_json(orient="table"))
"""
{
    "schema": {
        "fields": [{"name": "index", "type": "integer"},
                   {"name": "name", "type": "string"},
                   {"name": "age", "type": "integer"}],
        "primaryKey": ["index"],
        "pandas_version": "0.20.0"
    },
    "data": [{"index": 0, "name": "mashiro", "age": 17},
             {"index": 1, "name": "satori", "age": 17},
             {"index": 2, "name": "koishi", "age": 16},
             {"index": 3, "name": "nagisa", "age": 21}]
}
"""
print(df.to_json(orient="table", index=False))
"""
{
    "schema": {
        "fields": [{"name": "name", "type": "string"},
                   {"name": "age", "type": "integer"}],
        "pandas_version": "0.20.0"
    },
    "data": [{"name": "mashiro", "age": 17},
             {"name": "satori", "age": 17},
             {"name": "koishi", "age": 16},
             {"name": "nagisa", "age": 21}]
}
"""

2 . 转成dict

DataFrame也可以转成字典，转换成字典里面也有一个orient参数，里面有一部分和to_json是类似的。因为json这个数据结构本身就借鉴了python中的字典，是的你没有看错，json这种数据结构参考了python中的字典。

to_dict中的orient可以有如下取值：dict、list、series、split、records、index，默认是dict

• orient='dict'

from pprint import pprint
import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

pprint(df.to_dict(orient="dict"))
"""
{'age': {0: 17, 1: 17, 2: 16, 3: 21},
 'name': {0: 'mashiro', 1: 'satori', 2: 'koishi', 3: 'nagisa'}}
"""

• orient='list'

from pprint import pprint
import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

pprint(df.to_dict(orient="list"))
"""
{'age': [17, 17, 16, 21], 'name': ['mashiro', 'satori', 'koishi', 'nagisa']}
"""

• orient='series'

from pprint import pprint
import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

# 这种结构真的不常用，就是一个key对应一个series
pprint(df.to_dict(orient="series"))
"""
{'age': 
0    17
1    17
2    16
3    21
Name: age, dtype: int64,

'name': 0    mashiro
1     satori
2     koishi
3     nagisa
Name: name, dtype: object}
"""

• orient='split'

from pprint import pprint
import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

pprint(df.to_dict(orient="split"))
"""
{'columns': ['name', 'age'],
 'data': [['mashiro', 17], ['satori', 17], ['koishi', 16], ['nagisa', 21]],
 'index': [0, 1, 2, 3]}
"""

• orient='records'

from pprint import pprint
import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

pprint(df.to_dict(orient="records"))
"""
[{'age': 17, 'name': 'mashiro'},
 {'age': 17, 'name': 'satori'},
 {'age': 16, 'name': 'koishi'},
 {'age': 21, 'name': 'nagisa'}]
"""

• orient='index'

from pprint import pprint
import pandas as pd

df = pd.DataFrame({"name": ["mashiro", "satori", "koishi", "nagisa"],
                   "age": [17, 17, 16, 21]})

pprint(df.to_dict(orient="index"))
"""
{0: {'age': 17, 'name': 'mashiro'},
 1: {'age': 17, 'name': 'satori'},
 2: {'age': 16, 'name': 'koishi'},
 3: {'age': 21, 'name': 'nagisa'}}
"""

 

二 . python中的数据格式转成DataFrame

 1 . 字典转成DataFrame

import pandas as pd

data = {0: {'age': 17, 'name': 'mashiro'},
        1: {'age': 17, 'name': 'satori'},
        2: {'age': 16, 'name': 'koishi'},
        3: {'age': 21, 'name': 'nagisa'}}

df = pd.DataFrame.from_dict(data)
# 显然不是我们期待的格式
print(df)
"""
            0       1       2       3
age        17      17      16      21
name  mashiro  satori  koishi  nagisa
"""

df = pd.DataFrame.from_dict(data, orient="index")
print(df)
"""
   age     name
0   17  mashiro
1   17   satori
2   16   koishi
3   21   nagisa
"""

所以df.to_dict和pd.DataFrame.from_json实现的是相反的功能，但是from_dict中的orient参数只有两种选择，要么是index，要么是columns，默认是columns

from_records

from_records是专门针对外层是列表的数据

import pandas as pd

data = [{'age': 17, 'name': 'mashiro'},
        {'age': 17, 'name': 'satori'},
        {'age': 16, 'name': 'koishi'},
        {'age': 21, 'name': 'nagisa'}]

df = pd.DataFrame.from_records(data)
print(df)
"""
   age     name
0   17  mashiro
1   17   satori
2   16   koishi
3   21   nagisa
"""

其实这种数据就是to_dict(orient="records")生成的

 

标签：name,17,python,age,DataFrame,df,数据结构,orient,21
来源： https://www.cnblogs.com/zmc940317/p/13603108.html

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