Control Scrolling in Jetpack Compose

def max_by(lst, fn):
  return max(map(fn, lst))

Row {
    Button(onClick = {
        coroutineScope.launch {
            // 0 is the first item index
            scrollState.animateScrollToItem(0)
        }
    }) {
        Text("Scroll to the top")
    }

    Button(onClick = {
        coroutineScope.launch {
            // listSize - 1 is the last index of the list
            scrollState.animateScrollToItem(listSize - 1)
        }
    }) {
        Text("Scroll to the end")
    }
}

import androidx.compose.runtime.getValue
import androidx.compose.runtime.setValue

# Python program to illustrate 
# Finding common member in list  
# using 'in' operator 
list1=[1,2,3,4,5] 
list2=[6,7,8,9] 
for item in list1: 
    if item in list2: 
        print("overlapping")       
else: 
    print("not overlapping") 

PSA: How to View Variables in Lists
by 
Bradford Shelley
 Forum Level 2
created 4y ago (edited 3y ago ) in Developer Community
After having to play around with variables quite a bit in a recent project, I thought I'd share how to display variables on a list of Requested Items / Catalog Tasks. This applies to lists and related lists, as reports have their own method of displaying variables. Important note: This was performed on Fuji. Your experience may differ on older versions of ServiceNow.

Step 1 Identify the variables you'd like to display on your list, then copy the sys_id for each variable. This is as simple as heading to the Catalog Item, and jumping into the variable(s) in question. We'll need the sys_id to add the column into the list.

Step 2 Head over to System UI -> Lists

Step 3 Identify the list you'd like to display the variable(s) on. We're looking for one of two things here. Either the name of the view of the list you'd like to include the variable(s) on (I highly recommend creating a new view instead of using the Default view for this, as most likely the variables will apply to certain catalog items instead of every single one), or for a related list look at the Parent and Relationship columns for the table and name of the tab for the related list. Head into the list when you've found it.



Step 4 Create a new List Element(s) from the related list at, using "variables.sys_id" (no quotes) in the actual Element field on the List Element form. Create a new List Element for each variable you wish to add.



Step 5 Head over to the list where variables were just added. They won't show up immediately, so don't panic. Edit the list layout, and move around the variables to the spots you'd like them in the list (even if the position is already how you'd like it, move a variable one position up/down, then move it back and save). At this point, the variables should be visible!

Hope this helps at least one person out there. Take care!

fruit = {
  "elderberries": 1,
  "figs": 1,
  "apples": 2,
  "durians": 3,
  "bananas": 5,
  "cherries": 8,
  "grapes": 13
}

table_data = []
for k, v in fruit.items():
   table_data.append([k, v])

# house list of lists
house = [["hallway", 11.25], 
         ["kitchen", 18.0], 
         ["living room", 20.0], 
         ["bedroom", 10.75], 
         ["bathroom", 9.50]]
         
# Build a for loop from scratch
for x in house :
    #x[0] to access name of room
    #x[1] to access area in sqm
    print('the ' + x[0] + " is " + str(x[1]) + " sqm")

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'austria':'vienna' }
          
# Iterate over europe
for key, value in europe.items() :
    print('the capital of ' + str(key) + ' is ' + str(value))

# Import numpy as np

import numpy as np
#for x in my_array : #in 1D Numpy array
#for x in np.nditer(my_array) : #for 2D Numpy array

# For loop over np_height

for x in np_height:
    print(str(x) + " inches")

# For loop over np_baseball
for x in (np.nditer(np_baseball)):
    print(x)

#Iterating over a Pandas DataFrame is typically done with the iterrows()
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Iterate over rows of cars
for lab,row in cars.iterrows():
    print(lab)
    print(row)

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Adapt for loop
for lab, row in cars.iterrows() :
    print(lab + ": " + str(row['cars_per_cap']))

#Something new

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Code for loop that adds COUNTRY column
for lab, row in cars.iterrows():
    cars.loc[lab,'COUNTRY'] = row['country'].upper()


# Print cars
print(cars)

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Use .apply(str.upper)


cars['COUNTRY'] = cars['country'].apply(str.upper)
print(cars)

# Import numpy as np
import numpy as np

# Set the seed
np.random.seed(123)

# Generate and print random float
print(np.random.rand())

#Roll The Dice
# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Use randint() to simulate a dice
print(np.random.randint(1,7))
# Use randint() again
print(np.random.randint(1,7))

# Numpy is imported, seed is set

# Starting step
step = 50
# Roll the dice
dice = np.random.randint(1,7)
# Finish the control construct
if dice <= 2 :
    step = step - 1
elif dice <= 5 :
    step = step + 1
else:
    step = step + np.random.randint(1,7)

# Print out dice and step
print(dice)
print(step)

# Numpy is imported, seed is set

# Initialize random_walk
random_walk = [0]

# Complete the ___
for x in range(100) :
    # Set step: last element in random_walk
   
    step = random_walk[-1]

    # Roll the dice
    dice = np.random.randint(1,7)

    # Determine next step
    if dice <= 2:
        step = step - 1
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    # append next_step to random_walk
    random_walk.append(step)

# Print random_walk
print(random_walk)

#Not Going below zero
# Numpy is imported, seed is set

# Initialize random_walk
random_walk = [0]

for x in range(100) :
    step = random_walk[-1]
    dice = np.random.randint(1,7)

    if dice <= 2:
        # Replace below: use max to make sure step can't go below 0
        step = max(0, step - 1)
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    random_walk.append(step)

print(random_walk)