I'm quite new to PySpark and coming from SAS I still don't get how to handle parameters (or Macro Variables in SAS terminology).
I have a date parameter like "202105" and want to add it as a String Column to a Dataframe.
Something like this:
date = 202105
df = df.withColumn("DATE", lit('{date}'))
I think it's quite trivial but so far, I didn't find an exact answer to my problem, maybe it's just too trivial...
Hope you guys can help me out. Best regards
You can use string interpolations i.e. {}.format() (or) f'{}'.
Example:
df.withColumn("DATE", lit("{0}".format(date)))
df.withColumn("DATE", lit("{}".format(date)))
#or
df.withColumn('DATE', lit(f'{date}'))
Related
I have a problem, that I am not able to automatically solve since I just cannot find how to do it. I would like to extract the format of a datetime column that is visualized when a dataframe is printed.
I have a column within my dataframe that is of the type datetime.datetime. If I print the dataframe I get the following:
And if I print one value I get this:
I am not sure what the approach is to easily return the format of the values in the upper image. Just to be clear, I would like to have code that will return the format, that is shown in the dataframe, in datetime codes. In this example it should return: '%Y-%m-%d %H:%M:%S.%f'.
I am able to return this by first transforming the column to string values and then use the function _guess_datetime_format_for_array() from pandas.core.tools.datetimes, but this approach is a bit excessive in my opinion. Does anyone have a suggestion of a more easy solution?
The red highlight part is pd.read_csv, we can get a dataframe type object here;
Then the blue highlight part is a list with lambda function (we can filter account ID when reading the CSV file).
This method seems very smart, but a bit confusing to me. Could anyone explain how this could work as a filter? Thank you very much.
The [...] part is called indexing, and basically there you're just creating a function (a "lambda" function) and indexing the dataframe with it. What you're going to get out of it is all the rows where acct_id is OVIWFZA.
It's identical to this:
df = pd.read_csv('/content/drive/client.csv', nrows=5)
df = df[df['acct_id'] == 'OVIWFZA']
I have a table in pandas dataframe, and need to go through a column and check if I have that value in ADX. how can I do that? I was thinking of setting each entry in pandas as a variable, and call it in KQL. Any ideas how?
sth like this, but not sure how:
val=df['col_name'][0]
%%kql
table_name
| where value == $val
thanks!
Note sure I understand the requirement, best if you can specify a minimal example with the df in Python, and a table in ADX (using datatable operator).
Anyway, just fyi, you can copy variables from Jupyter to Kusto using let, see example
I have a dataframe as such:
I wish to transpose it to:
I understand that this might be a basic question, therefore, if someone could direct me to the correct references so I can try to figure out how to do so in pandas.
try with melt() and set_index():
out=(df.melt(id_vars=['Market','Product'],var_name='Date',value_name='Value')
.set_index('Date'))
If needed use:
out.index.name=None
Now If you print out you will get your desired output
I want to change the date format in a pandas dataframe. I foud out that it should be something like the code below:
df['date'] = pd.to_datetime(df['date']).dt.strftime("%d-%m-%Y")
The problem is that I get a "SettingWithCopyWarning" everytime I do this. Does somebody know a proper way to do this?