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<p>我创建了一个lambda函数,该函数从S3下载数据,然后执行合并,然后将数据重新上传回S3,但我得到了这个结果</p>
<p>错误{
“错误消息”:“2020-05-18T23:23:27.556Z 37233f48-18ea-43eb-9030-3e8a2bf62048任务在3.00秒后超时”
}</p>
<p><strong><em>当我删除45和58之间的行时,它工作得很好</em></strong></p>
<p><a href="https://i.stack.imgur.com/JAWRX.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/JAWRX.png" alt="enter image description here"/></a>
<a href="https://ideone.com/RvOmPS" rel="nofollow noreferrer">https://ideone.com/RvOmPS</a></p>
<p>作为pd进口熊猫
将numpy作为np导入
导入时间
从io导入StringIO#python3;Python2:BytesIO
进口boto3
导入S3F
从botocore.exceptions导入NoCredentialsError</p>
<p>def lambda_处理程序(事件、上下文):</p>
<pre><code># Dataset 1
# loading the data
df1 = pd.read_csv("https://i...content-available-to-author-only...s.com/Minimum+Wage+Data.csv",encoding= 'unicode_escape')
# Renaming the columns.
df1.rename(columns={'High.Value': 'min_wage_by_law', 'Low.Value': 'min_wage_real'}, inplace=True)
# Removing all unneeded values.
df1 = df1.drop(['Table_Data','Footnote','High.2018','Low.2018'], axis=1)
df1 = df1.loc[df1['Year']>1969].copy()
# ---------------------------------
# Dataset 2
# Loading from the debt S3 bucket
df2 = pd.read_csv("https://i...content-available-to-author-only...s.com/USGS_Final_File.csv")
#Filtering getting the range in between 1969 and 2018.
df2 = df2.loc[df2['Year']>1969].copy()
df2 = df2.loc[df2['Year']<2018].copy()
df2.rename(columns={'Real State Growth %': 'Real State Growth','Population (million)':'Population Mil'}, inplace=True)
# Cleaning the data
df2['State Debt'] = df2['State Debt'].str.replace(',', '')
df2['Local Debt'] = df2['Local Debt'].str.replace(',', '')
df2["State and Local Debt"] = df2["State and Local Debt"].str.replace(',', '')
df2["Gross State Product"] = df2["Gross State Product"].str.replace(',', '')
# Cast to Floating
df2[["State Debt","Local Debt","State and Local Debt","Gross State Product"]] = df2[[ "State Debt","Local Debt","State and Local Debt","Gross State Product"]].apply(pd.to_numeric)
# --------------------------------------------
# Merge the data through an inner join.
full = pd.merge(df1,df2,on=['State','Year'])
#--------------------------------------------
filename = '/tmp/'#specify location of s3:/{my-bucket}/
file= 'debt_and_wage' #name of file
datetime = time.strftime("%Y%m%d%H%M%S") #timestamp
filenames3 = "%s%s%s.csv"%(filename,file,datetime) #name of the filepath and csv file
full.to_csv(filenames3, header = True)
## Saving it on AWS
s3 = boto3.resource('s3',aws_access_key_id='accesskeycantshare',aws_secret_access_key= 'key')
s3.meta.client.upload_file(filenames3, 'information-arch',file+datetime+'.csv')
</code></pre>