[英]How do I loop through dictionary to get output for them in separate rows in my final dataframe?
我是 python 新手，并尝试处理数据循环：
我正在使用 sendgrid api 类别 stats 端点，并且我成功获取了一个 API 密钥的输出。
我的代码：
import pandas as pd
import json
from pandas.io.json import json_normalize
import datetime
yesterday = datetime.date.fromordinal(datetime.date.today().toordinal()-1).strftime("%F")
import http.client
conn = http.client.HTTPSConnection("api.sendgrid.com")
payload = "{}"
keys = {
#"CF_APHI_marketing" : "SG.UdhzjmjYR**",
# "CF_APHI_operational": "SG.FO0b2O*",
# "CF_HPI_marketing": "SG._dnhFlnrSL-**",
# "CF_HPI_operational": "SG.4duN1bHiT9G1**",
# "CF_PPI_operational": "SG.F3zH1XEB**",
# "CF_PP_operational": "SG.9TcrDUAkSLWT**",
# "CF_PP_marketing" : "SG.GD5wMhE_**",
"CF_SandBox" : "SG.eDU7TY**",
# "CF_PKN_operational" : "SG.JsIws8hLT5iYxry0oJlGqw.**",
# "CF_PTC_operational" : "SG.2-dxeY8BRsabipDlRuvZMg.-**",
# "CF_SP_operational" : "SG.tSF_YmxlRkmmsiHNrK2CWQ.xtyhO4UX3U--**",
# "CF_SP_marketing" : "SG.KDuHYR-bRq-T74UexS4WpA.**-**",
# "CF_EI_operational" : "SG.2TRc1VQMT-WDirJG-U-PtQ.sAx-**",
# "CF_D_operational" : "SG.KNFxNavYQ1yJwGTpnIDKxA.**-**"
}
df = [] # Create new Dataframe
for name, value in keys.items():
headers = { 'authorization': "Bearer " + value }
conn.request("GET", "/v3/categories/stats/sums?aggregated_by=&start_date={d}&end_date={d}".format(d=yesterday).format(d=yesterday), payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
d = json.loads(data.decode("utf-8"))
c=d['stats']
# row = d['stats'][0]['name']
# Add Brand to data row here with 'name'
df.append(c) # Load data row into df
当我调用 df 时，我的输出是：
df输出[8]：
[[{'type': 'category',
'name': 'Claim Notification Email',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 5,
'invalid_emails': 0,
'opens': 0,
'processed': 0,
'requests': 5,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}},
{'type': 'category',
'name': 'Records Request 2',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 1,
'invalid_emails': 0,
'opens': 0,
'processed': 0,
'requests': 1,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}},
{'type': 'category',
'name': 'Records Request 1',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 1,
'invalid_emails': 0,
'opens': 0,
'processed': 0,
'requests': 1,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}},
{'type': 'category',
'name': 'Appeal Received Email',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 0,
'invalid_emails': 0,
'opens': 1,
'processed': 0,
'requests': 0,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}}]]
我需要我的最终数据框看起来像：
Brand CF_APHI_marketing
name blocks bounce_drops bounces ... unique_opens unsubscribe_dropsunsubscribes
Appeal Received Email 0 7 14 9 ... 1240 0
Records Request 18 12 7 2000 0 0
Records Request2 2 10 8 3333 1 0
基本上想要获得上述 13 个品牌中每个品牌的所有类别名称的品牌（键）输出。 有没有更好的方法来创建这个数据框？
我试过
sendgrid = pd.DataFrame(df)
但是当我将它导出到 csv 时，我没有收到像上面那样的输出，只是得到了一些json文本
如果：
df = [[{'type': 'category',
'name': 'Claim Notification Email',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 5,
'invalid_emails': 0,
'opens': 0,
'processed': 0,
'requests': 5,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}},
{'type': 'category',
'name': 'Records Request 2',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 1,
'invalid_emails': 0,
'opens': 0,
'processed': 0,
'requests': 1,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}},
{'type': 'category',
'name': 'Records Request 1',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 1,
'invalid_emails': 0,
'opens': 0,
'processed': 0,
'requests': 1,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}},
{'type': 'category',
'name': 'Appeal Received Email',
'metrics': {'blocks': 0,
'bounce_drops': 0,
'bounces': 0,
'clicks': 0,
'deferred': 0,
'delivered': 0,
'invalid_emails': 0,
'opens': 1,
'processed': 0,
'requests': 0,
'spam_report_drops': 0,
'spam_reports': 0,
'unique_clicks': 0,
'unique_opens': 0,
'unsubscribe_drops': 0,
'unsubscribes': 0}}]]
尝试这个：
df = pd.DataFrame(df[0])
df_new = df[['type', 'name']]
df_metric =pd.DataFrame(list(df['metrics'].values))
sendgrid = pd.concat([df_new, df_metric], axis=1, sort=False)
sendgrid.set_index('name', inplace = True)
sendgrid.to_csv("sendgrid.csv")
你应该得到：
type blocks bounce_drops bounces clicks \
name
Claim Notification Email category 0 0 0 0
Records Request 2 category 0 0 0 0
Records Request 1 category 0 0 0 0
Appeal Received Email category 0 0 0 0
deferred delivered invalid_emails opens \
name
Claim Notification Email 0 5 0 0
Records Request 2 0 1 0 0
Records Request 1 0 1 0 0
Appeal Received Email 0 0 0 1
processed requests spam_report_drops \
name
Claim Notification Email 0 5 0
Records Request 2 0 1 0
Records Request 1 0 1 0
Appeal Received Email 0 0 0
spam_reports unique_clicks unique_opens \
name
Claim Notification Email 0 0 0
Records Request 2 0 0 0
Records Request 1 0 0 0
Appeal Received Email 0 0 0
unsubscribe_drops unsubscribes
name
Claim Notification Email 0 0
Records Request 2 0 0
Records Request 1 0 0
Appeal Received Email 0 0
IIUC，我们可以遍历您的嵌套列表并创建一个数据框，然后对其进行整形
注意我已将您的变量从 df 更改为 json_d 以使其更加明确，因为它根本不是数据帧。
dfs = []
for _ in json_d:
for inner_ in _:
df = pd.DataFrame.from_dict(inner_)
dfs.append(df)
new_df = pd.concat(dfs)
print(new_df)
type name metrics
blocks category Claim Notification Email 0
bounce_drops category Claim Notification Email 0
bounces category Claim Notification Email 0
clicks category Claim Notification Email 0
deferred category Claim Notification Email 0
... ... ... ...
spam_reports category Appeal Received Email 0
unique_clicks category Appeal Received Email 0
unique_opens category Appeal Received Email 0
unsubscribe_drops category Appeal Received Email 0
unsubscribes category Appeal Received Email 0
然后只是一个简单的重塑到你的目标df。
df = pd.crosstab(new_df.name, new_df.index, new_df.metrics, aggfunc=lambda x: x)
del df.columns.name
print(df)
blocks bounce_drops bounces clicks deferred \
name
Appeal Received Email 0 0 0 0 0
Claim Notification Email 0 0 0 0 0
Records Request 1 0 0 0 0 0
Records Request 2 0 0 0 0 0
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