2023年2月16日 16:32:22go评论57阅读模式

英文:

creating pandas df from disctionary in python

问题

我从API中获取的数据如下：

&gt; {'Message': {'Success': True, 'ErrorMessage': ''},
&gt; 'StoresAttributes': [{'StoreCode': '1004',
&gt; 'Categories': [{'Code': 'Lctn',
&gt; 'Attribute': {'Code': 'Long', 'Value': '16.99390523395146'}},
&gt; {'Code': 'Lctn',
&gt; 'Attribute': {'Code': 'Lat', 'Value': '52.56718450856377'}},
&gt; {'Code': 'Offr', 'Attribute': {'Code': 'Bake', 'Value': 'True'}},
&gt; {'Code': 'Pay', 'Attribute': {'Code': 'SCO', 'Value': 'True'}}]},
&gt; {'StoreCode': '1005',
&gt; 'Categories': [{'Code': 'Lctn',
&gt; 'Attribute': {'Code': 'Long', 'Value': '14.2339250'}},
&gt; {'Code': 'Lctn', 'Attribute': {'Code': 'Lat', 'Value': '53.8996090'}},
&gt; {'Code': 'Offr', 'Attribute': {'Code': 'Bake', 'Value': 'True'}},
&gt; {'Code': 'Pay', 'Attribute': {'Code': 'SCO', 'Value': 'True'}},
&gt; {'Code': 'Offr', 'Attribute': {'Code': 'Bchi', 'Value': 'True'}}]}

我想从中创建一个数据框。我尝试使用循环或pd.DataFrame()函数，但它没有正常工作。

我想实现的是具有以下连续列的数据框：

StoreCode: 1004,
Long: 16.99,
Lat: 52.56,
Bake: True.

是否有人可以帮忙？

下面是我从json_normalize中获取的结果的屏幕截图。

error

英文:

I have data coming from API like below:

&gt; {&#39;Message&#39;: {&#39;Success&#39;: True, &#39;ErrorMessage&#39;: &#39;&#39;},
&gt; &#39;StoresAttributes&#39;: [{&#39;StoreCode&#39;: &#39;1004&#39;,
&gt; &#39;Categories&#39;: [{&#39;Code&#39;: &#39;Lctn&#39;,
&gt; &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Long&#39;, &#39;Value&#39;: &#39;16.99390523395146&#39;}},
&gt; {&#39;Code&#39;: &#39;Lctn&#39;,
&gt; &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Lat&#39;, &#39;Value&#39;: &#39;52.56718450856377&#39;}},
&gt; {&#39;Code&#39;: &#39;Offr&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Bake&#39;, &#39;Value&#39;: &#39;True&#39;}},
&gt; {&#39;Code&#39;: &#39;Pay&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;SCO&#39;, &#39;Value&#39;: &#39;True&#39;}}]},
&gt; {&#39;StoreCode&#39;: &#39;1005&#39;,
&gt; &#39;Categories&#39;: [{&#39;Code&#39;: &#39;Lctn&#39;,
&gt; &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Long&#39;, &#39;Value&#39;: &#39;14.2339250&#39;}},
&gt; {&#39;Code&#39;: &#39;Lctn&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Lat&#39;, &#39;Value&#39;: &#39;53.8996090&#39;}},
&gt; {&#39;Code&#39;: &#39;Offr&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Bake&#39;, &#39;Value&#39;: &#39;True&#39;}},
&gt; {&#39;Code&#39;: &#39;Pay&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;SCO&#39;, &#39;Value&#39;: &#39;True&#39;}},
&gt; {&#39;Code&#39;: &#39;Offr&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Bchi&#39;, &#39;Value&#39;: &#39;True&#39;}}]},

And I want to make data frame from it. I have tried with loop or pd.DataFrame() function but it didn't work properly.

What I want to achieve is df with subsequent columns:

StoreCode: 1004,
Long: 16,99,
Lat: 52,56,
Bake: True.

Can please anyone help?

Below screen with my result from json_normalize

error

答案1

得分: 2

你可以使用 json_normalize 然后 pivot：

import pandas as pd

data = {'Message': {'Success': True, 'ErrorMessage': ''}, 'StoresAttributes': [{'StoreCode': '1004', 'Categories': [{'Code': 'Lctn', 'Attribute': {'Code': 'Long', 'Value': '16.99390523395146'}}, {'Code': 'Lctn', 'Attribute': {'Code': 'Lat', 'Value': '52.56718450856377'}}, {'Code': 'Offr', 'Attribute': {'Code': 'Bake', 'Value': 'True'}}, {'Code': 'Pay', 'Attribute': {'Code': 'SCO', 'Value': 'True'}}]}, {'StoreCode': '1005', 'Categories': [{'Code': 'Lctn', 'Attribute': {'Code': 'Long', 'Value': '14.2339250'}}, {'Code': 'Lctn', 'Attribute': {'Code': 'Lat', 'Value': '53.8996090'}}, {'Code': 'Offr', 'Attribute': {'Code': 'Bake', 'Value': 'True'}}, {'Code': 'Pay', 'Attribute': {'Code': 'SCO', 'Value': 'True'}}, {'Code': 'Offr', 'Attribute': {'Code': 'Bchi', 'Value': 'True'}}]}]}

df = pd.json_normalize(data['StoresAttributes'], meta='StoreCode', record_path='Categories')
df.pivot(columns='Attribute.Code', values='Attribute.Value', index='StoreCode')

输出：

Attribute.Code  Bake  Bchi                Lat               Long   SCO
StoreCode
1004            True   NaN  52.56718450856377  16.99390523395146  True
1005            True  True         53.8996090         14.2339250  True

英文:

You can use json_normalize then pivot:

import pandas as pd

data = {&#39;Message&#39;: {&#39;Success&#39;: True, &#39;ErrorMessage&#39;: &#39;&#39;}, &#39;StoresAttributes&#39;: [{&#39;StoreCode&#39;: &#39;1004&#39;, &#39;Categories&#39;: [{&#39;Code&#39;: &#39;Lctn&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Long&#39;, &#39;Value&#39;: &#39;16.99390523395146&#39;}}, {&#39;Code&#39;: &#39;Lctn&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Lat&#39;, &#39;Value&#39;: &#39;52.56718450856377&#39;}}, {&#39;Code&#39;: &#39;Offr&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Bake&#39;, &#39;Value&#39;: &#39;True&#39;}}, {&#39;Code&#39;: &#39;Pay&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;SCO&#39;, &#39;Value&#39;: &#39;True&#39;}}]}, {&#39;StoreCode&#39;: &#39;1005&#39;, &#39;Categories&#39;: [{&#39;Code&#39;: &#39;Lctn&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Long&#39;, &#39;Value&#39;: &#39;14.2339250&#39;}}, {&#39;Code&#39;: &#39;Lctn&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Lat&#39;, &#39;Value&#39;: &#39;53.8996090&#39;}}, {&#39;Code&#39;: &#39;Offr&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Bake&#39;, &#39;Value&#39;: &#39;True&#39;}}, {&#39;Code&#39;: &#39;Pay&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;SCO&#39;, &#39;Value&#39;: &#39;True&#39;}}, {&#39;Code&#39;: &#39;Offr&#39;, &#39;Attribute&#39;: {&#39;Code&#39;: &#39;Bchi&#39;, &#39;Value&#39;: &#39;True&#39;}}]}]}
    
df = pd.json_normalize(data[&#39;StoresAttributes&#39;], meta=&#39;StoreCode&#39;, record_path=&#39;Categories&#39;)
df.pivot(columns=&#39;Attribute.Code&#39;, values=&#39;Attribute.Value&#39;, index=&#39;StoreCode&#39;)

Output:

Attribute.Code  Bake  Bchi                Lat               Long   SCO
StoreCode
1004            True   NaN  52.56718450856377  16.99390523395146  True
1005            True  True         53.8996090         14.2339250  True

答案2

得分: 0

你可以像这样使用`json_normalize()`函数：

```python
data = [
     {"id": 1, "name": {"first": "Coleen", "last": "Volk"}},
     {"name": {"given": "Mark", "family": "Regner"}},
     {"id": 2, "name": "Faye Raker"},
 ]

pd.json_normalize(data)

输出:

    id name.first name.last name.given name.family        name
0  1.0     Coleen      Volk        NaN         NaN         NaN
1  NaN        NaN       NaN       Mark      Regner         NaN
2  2.0        NaN       NaN        NaN         NaN  Faye Raker

你可以点击下面的链接了解更多关于json_normalize()函数的信息。

点击这里


<details>
<summary>英文:</summary>

You can use `json_normalize()` function like this:

```python
data = [
     {&quot;id&quot;: 1, &quot;name&quot;: {&quot;first&quot;: &quot;Coleen&quot;, &quot;last&quot;: &quot;Volk&quot;}},
     {&quot;name&quot;: {&quot;given&quot;: &quot;Mark&quot;, &quot;family&quot;: &quot;Regner&quot;}},
     {&quot;id&quot;: 2, &quot;name&quot;: &quot;Faye Raker&quot;},
 ]

pd.json_normalize(data)

Output:

    id name.first name.last name.given name.family        name
0  1.0     Coleen      Volk        NaN         NaN         NaN
1  NaN        NaN       NaN       Mark      Regner         NaN
2  2.0        NaN       NaN        NaN         NaN  Faye Raker

You can refer to the below link to know more about the json_normalize() function.

CLICK HERE

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