dob

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: When CSV is read as dataframe in spark, all the columns are read as string. Is there any way to get the actual type of column? I have the following csv file Name,Department,years_of_experience,DOB Sam,Software,5,1990-10-10 Alex,Data Analytics,3,1992-10-10 I've read the CSV using the below code val df = sqlContext. read. format("com.databricks.spark.csv"). option("header", "true"). option("inferSchema", "true"). load(sampleAdDataS3Location) df.schema All the columns are read as string. I expect the column years_of_experience to be read as int

对于已经是datetime格式的 x1 x1 = pd.to_datetime( market.DATE, format=”%Y-%m-%d-%H-%M-%S”) x1 = x1.dt.strftime(‘%m/%d/%Y’) source： https://stackoverflow.com/questions/38067704/how-to-change-the-datetime-format-in-pandas You can use dt.strftime, if need convert datetime to other formats, but then dtype of column is object (string): import pandas as pd df = pd.DataFrame({‘DOB’: {0: ‘26/1/2016 ‘, 1: ‘26/1/2016 ‘}}) print (df) DOB 0 26/1/2016 1 26/1/2016 df[‘DOB’] = pd.to_datetime(df.DOB) print (df) DOB 0 2016-01-26 1 2016-01-26 df[‘DOB1’] = df[‘DOB’].dt.strftime(‘%m/%d/%Y’) print (df) DOB DOB1 0 2016-01