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Iterate through rows in DataFrame and transform one to many


Iterate through rows in DataFrame and transform one to many

By : TonyDS
Date : November 21 2020, 03:00 PM
may help you . You can filter matching-conditions dataframes and then finally union all of them.
code :
import org.apache.spark.sql.functions._
val condition1DF = df.filter($"age" > 60).withColumn("condition", lit(1))
val condition2DF = df.filter(length($"name") <= 4).withColumn("condition", lit(2))

val finalDF = condition1DF.union(condition2DF)
+----+---+---------+
|name|age|condition|
+----+---+---------+
|Mary|70 |1        |
|Paul|60 |2        |
|Mary|70 |2        |
+----+---+---------+


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Iterate over rows in a Dataframe and compare it to the rest of the rows

Iterate over rows in a Dataframe and compare it to the rest of the rows


By : Nicolas Innocenti
Date : March 29 2020, 07:55 AM
wish help you to fix your issue If your desired function (f) has side-effects, i'd use df.iterrows() and write the function in python.
code :
for index, row in df.iterrows():
  # Do stuff
df['tagged'] = df.apply(lambda row: <<condition goes here>>, axis=1)
tagged_rows = df[df['tagged'] == True]
df = df[df['tagged'] != True]
How to merge rows of pandas DataFrame into list and transform this DataFrame into dict

How to merge rows of pandas DataFrame into list and transform this DataFrame into dict


By : Carter
Date : March 29 2020, 07:55 AM
should help you out I have a DataFrame , Merge the values:
code :
df.groupby('col1').col2.apply(list)
#col1
#a    [1, 4]
#b    [2, 5]
#c       [3]
#Name: col2, dtype: object
df.groupby('col1').col2.apply(list).to_dict()
# {'a': [1, 4], 'b': [2, 5], 'c': [3]}
How to iterate through rows of a DataFrame and add those rows to a blank DataFrame?

How to iterate through rows of a DataFrame and add those rows to a blank DataFrame?


By : cristina
Date : March 29 2020, 07:55 AM
To fix this issue If are trying to append dataframe df1 on empty dataframe df2 you can use concat function of pandas.
code :
test = pd.concat([df1, test], axis = 0)
Transform users (repeated over multiple rows) and items in a dataframe into a label binarized dataframe

Transform users (repeated over multiple rows) and items in a dataframe into a label binarized dataframe


By : user3300354
Date : March 29 2020, 07:55 AM
I wish this helpful for you I have a DataFrame that looks like this , A solution with some standard scikit-learn:
code :
from sklearn.feature_extraction.text import CountVectorizer

def squish(df, user='user', item='item'):
    df = df.groupby([user])[item].apply(lambda x: ','.join(x))
    X = pd.DataFrame(df)[item]
    return X

cv = CountVectorizer(tokenizer=lambda x: x.split(','))
X = squish(df)
cv.fit_transform(X).todense()
# matrix([[1, 1, 1],
#         [1, 0, 0],
#         [0, 0, 1],
#         [0, 1, 1]], dtype=int64)
new_user = pd.DataFrame([
    ['c', 5],
    ['d', 5]
], columns=['item', 'user'])

X_new = squish(new_user)
cv.transform(X_new).todense()
# matrix([[0, 0, 1]])
Apache Spark: Iterate rows of dataframe and create new dataframe through MutableList (Scala)

Apache Spark: Iterate rows of dataframe and create new dataframe through MutableList (Scala)


By : DMax
Date : March 29 2020, 07:55 AM
I wish did fix the issue. typical beginner mistake: tList only lives on the driver, it cannot be updated from the executor-side code. Thats not how you create a dataframe from an existing dataframe. Use transformations/aggregation instead.
In you case you can do it with build-in Dataframe API functions split and size:
code :
import org.apache.spark.sql.functions._

val transformedDf = df
  .select(
      $"id",
      size(split($"body"," "))).as("cnt")
  )
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