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Looking to find values in one dataframe and input them into another dataframe


Looking to find values in one dataframe and input them into another dataframe

By : Błażej Podyma
Date : November 21 2020, 03:00 PM
I think the issue was by ths following , So, I have 5 dataframes that I need to loop through and they all follow a similar format: , Ended up figuring it out:
code :
for i in final_inds:
    for j in range(0,5):
        try:
            cluster_values = all_dfs[j].loc[i,"clusters"]
        except:
            cluster_values = -1

        final_df.loc[i, cols[j]] = cluster_values

final_df.head()


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Using column header and values from one dataframe to find weights in another dataframe

Using column header and values from one dataframe to find weights in another dataframe


By : Whatthigo
Date : March 29 2020, 07:55 AM
I wish did fix the issue. One option would be to transform table1 to long format, merge with table2 on parameters and values, and then transform it back to wide format:
code :
In [85]: pd.merge(pd.melt(df1, id_vars='ID'), df2,
                  left_on=['variable', 'value'], right_on=['parameter', 'value']
                   ).pivot('ID', 'parameter', 'weight')
Out[85]:
parameter  param1  param2  param3
ID
A12            13      21      49
B15            10      39      49
B20            13      39      49
How to find distinct values for different groups on a dataframe in Pyspark and recode the dataframe

How to find distinct values for different groups on a dataframe in Pyspark and recode the dataframe


By : Krejdom
Date : March 29 2020, 07:55 AM
this will help I have a big dataframe, the dataframe contain groups of people which are flag in the variable called "groups". , Hope this helps!
code :
import pyspark.sql.functions as f
from pyspark.sql.types import StringType

df = sc.parallelize([
    [0, 2189, 25, 148, 'car'],
    [0, 2192, 34, 148, 'house'],
    [1, 2193, 37, 148, 'car'],
    [1, 2194, 38, 148, 'house'],
    [1, 2196, 54, 148, 'laptop'],
    [1, 2197, 27, 148, 'laptop'],
    [0, 2198, 44, 148, 'laptop']
]).toDF(('gender', 'postcode', 'age', 'groups', 'bought'))
df.show()

df1 = df.groupBy("groups").agg(f.collect_set("bought")).withColumnRenamed("collect_set(bought)","bought")
df2 = df.groupBy("groups").agg(f.min("age"), f.max("age")). \
    withColumn("age", f.concat(f.col("min(age)"), f.lit("-"), f.col("max(age)"))).select("groups","age")
df3 = df.groupBy("groups").agg(f.min("postcode"), f.max("postcode")). \
    withColumn("postcode", f.concat(f.col("min(postcode)"), f.lit("-"), f.col("max(postcode)"))).select("groups","postcode")
def modify_values(l):
    if l == [0, 1]:
        return "person"
    else:
        if l == [0]:
            return "male"
        else:
            return "female"
modified_val = f.udf(modify_values, StringType())
df4 = df.groupBy("groups").agg(f.collect_set("gender")).withColumn("gender",modified_val("collect_set(gender)")).select("groups","gender")

merged_df = df1.join(df2, "groups").join(df3, "groups").join(df4, "groups")
merged_df.show()
+------+--------------------+-----+---------+------+
|groups|              bought|  age| postcode|gender|
+------+--------------------+-----+---------+------+
|   148|[laptop, house, car]|25-54|2189-2198|person|
+------+--------------------+-----+---------+------+
How to find maximum values in DataFrame and return a resulting DataFrame

How to find maximum values in DataFrame and return a resulting DataFrame


By : Johan Berglund
Date : March 29 2020, 07:55 AM
like below fixes the issue You can try to use .groupby method. It works similar groupby in sql and returns a dataframe.
After grouping, you define operations to the groups. There is the max operation for the Ptot (as you want it) and you can return mean for the others.
code :
df_all_q.groupby('Qtot').agg({'Ptot': 'max', 'Q_G1': 'mean',
'Q_G2': 'mean', 'P_G1': 'mean', 'P_G2': 'mean'}).reset_index()
Get unique values of a dataframe for select input choices but only when the dataframe was defined in server.ui not globa

Get unique values of a dataframe for select input choices but only when the dataframe was defined in server.ui not globa


By : user3554944
Date : March 29 2020, 07:55 AM
help you fix your problem Instead of using selectInput in the UI, you can instead use UIoutput in the UI and then define the UI element within the server function using renderUI.
So given that you have defined a UIOutput element called otn_race_selection_op, then we can define that as a selectInput object using the below code. Here getData is a reactive element that updates itself to the latest data. So based on that, you can modify the input choices for your selectInput object
code :

output$otn_race_selection_op <- renderUI({


    df <- getData() 

    options <- sort(unique(df$Race))

    selectInput(
      inputId = "otn_race_selection",
      label = "Race",
      choices = c("All", options)
      ,
      selected = "All"
    )

  })


Find identical values in a column of a dataframe and create a new dataframe with each duplicate

Find identical values in a column of a dataframe and create a new dataframe with each duplicate


By : user3611227
Date : March 29 2020, 07:55 AM
this will help If I understood you correctly, something like this will help. Let's assume df_customer is your dataframe.
Code:
code :
cntr=1
for i in df_customer['Account'].unique():
    locals()['df_customer'+str(cntr)]=df_customer[df_customer['Account'] == i]
    cntr += 1
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