DataFrame和python中数据结构互相转换
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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
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