use .dt.date
df[‘column’] = pd.to_datetime(df[‘column’], format=’%Y-%m-%d’).dt.date
Source-
https://stackoverflow.com/questions/16176996/keep-only-date-part-when-using-pandas-to-datetime
use .dt.date
df[‘column’] = pd.to_datetime(df[‘column’], format=’%Y-%m-%d’).dt.date
Source-
https://stackoverflow.com/questions/16176996/keep-only-date-part-when-using-pandas-to-datetime
pandasdf[“Col1”]= pandasdf[“Col1”].astype(str)
width = 5
pandasdf[“Col1”]= pandasdf[“Col1”].str.zfill(width)
https://stackoverflow.com/questions/23836277/add-leading-zeros-to-strings-in-pandas-dataframe
%pythondf=spark.sql(“select * from name_csv”)
display(df.select(“*”))
pandas_df = df.toPandas()
Creating SQL Table using Spark
%python
acc_1=spark.sql(“create table test_spark as select columns, column,columnc from table where to_date(ac_opn_dt) < ‘2012-07-01’ )”)
# Given pandas dataframe, return a spark’s dataframe.
def pandas_to_spark(pandas_df):
columns = list(pandas_df.columns)
types = list(pandas_df.dtypes)
struct_list = []
for column, typo in zip(columns, types):
struct_list.append(define_structure(column, typo))
p_schema = StructType(struct_list)
return sqlContext.createDataFrame(pandas_df, p_schema)
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Seems like many different types of time series curves that can be used for forecasting hospital resources across different countries