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February 18, 2019 05:29
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Get billing details of last 40 days using boto
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import boto3 | |
import datetime | |
import pandas as pd | |
import numpy as np | |
now = datetime.datetime.utcnow() | |
start = (now - datetime.timedelta(days=40)).strftime("%Y-%m-%d") | |
end = now.strftime("%Y-%m-%d") | |
cd = boto3.client("ce", | |
aws_access_key_id="XXX", aws_secret_access_key="XXX",) | |
results = [] | |
token = None | |
while True: | |
if token: | |
kwargs = {"NextPageToken": token} | |
else: | |
kwargs = {} | |
data = cd.get_cost_and_usage( | |
TimePeriod={"Start": start, "End": end}, | |
Granularity="DAILY", | |
Metrics=["UnblendedCost"], | |
GroupBy=[ | |
{"Type": "DIMENSION", "Key": "LINKED_ACCOUNT"}, | |
{"Type": "DIMENSION", "Key": "SERVICE"}, | |
], | |
**kwargs | |
) | |
results += data["ResultsByTime"] | |
token = data.get("NextPageToken") | |
if not token: | |
break | |
tp = list() | |
ai = list() | |
sn = list() | |
am = list() | |
for result_by_time in results: | |
for group in result_by_time["Groups"]: | |
TimePeriod = result_by_time["TimePeriod"]["Start"] | |
account_id = group["Keys"][0] | |
service_name = group["Keys"][1] | |
amount = group["Metrics"]["UnblendedCost"]["Amount"] | |
tp.append(TimePeriod) | |
ai.append(account_id) | |
sn.append(service_name) | |
am.append(amount) | |
df = pd.DataFrame([tp, ai, sn, am]).T | |
df.columns = ["date", "account_no", "service_name", "amount"] | |
df.to_csv("myreport.csv") | |
## EDA | |
import janitor | |
from sklearn.feature_selection import VarianceThreshold | |
df = pd.read_csv("myreport.csv") | |
ndf = ( | |
df.groupby(["date", "account_no", "service_name"])["amount"].sum().unstack() | |
) | |
ndf1 = ndf.astype(np.float) | |
ndf1 = ndf1.fillna(0) | |
qconstant_filter = VarianceThreshold(threshold=1.01) | |
ndf2 = qconstant_filter.fit_transform(ndf1) | |
mycols = ndf1.columns[qconstant_filter.get_support()] | |
report = pd.DataFrame(ndf2, columns=mycols) | |
ndf1 = ndf1.reset_index() | |
report["date"] = ndf1["date"] | |
df = report.rename_axis(index=None, columns=None) | |
df = df.set_index("date") | |
df.plot(kind="bar") | |
df = df[(df > 1).any(axis=1)] | |
df = df.clean_names( | |
strip_underscores=True, case_type="lower", remove_special=True | |
).limit_column_characters(18) |
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