```# numpy and matplotlib imported, seed set

# Simulate random walk 500 times
all_walks = []
for i in range(500) :
random_walk = 
for x in range(100) :
step = random_walk[-1]
dice = np.random.randint(1,7)
if dice <= 2:
step = max(0, step - 1)
elif dice <= 5:
step = step + 1
else:
step = step + np.random.randint(1,7)
if np.random.rand() <= 0.001 :
step = 0
random_walk.append(step)
all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))

# Select last row from np_aw_t: ends
ends = np_aw_t[-1, :]

# Plot histogram of ends, display plot
plt.hist(ends)
plt.show()
```
```# Numpy is imported; seed is set

# Initialize all_walks (don't change this line)
all_walks = []

# Simulate random walk 10 times
for i in range(10):

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

if dice <= 2:
step = max(0, step - 1)
elif dice <= 5:
step = step + 1
else:
step = step + np.random.randint(1,7)
random_walk.append(step)

# Append random_walk to all_walks
all_walks.append(random_walk)

# Print all_walks
print(all_walks)

#####################################################################
# numpy and matplotlib imported, seed set

# Simulate random walk 250 times
all_walks = []
for i in range(250) :
random_walk = 
for x in range(100) :
step = random_walk[-1]
dice = np.random.randint(1,7)
if dice <= 2:
step = max(0, step - 1)
elif dice <= 5:
step = step + 1
else:
step = step + np.random.randint(1,7)

# Implement clumsiness
if np.random.rand() <= 0.001 :
step = 0

random_walk.append(step)
all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))
plt.plot(np_aw_t)
plt.show()

```
```# Numpy is imported, seed is set

# Initialization
random_walk = 

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

if dice <= 2:
step = max(0, step - 1)
elif dice <= 5:
step = step + 1
else:
step = step + np.random.randint(1,7)

random_walk.append(step)

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Plot random_walk
plt.plot(random_walk)

# Show the plot
plt.show()```
```# Numpy is imported, seed is set

# Initialize random_walk
random_walk = 

# 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 = 

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)
```
```# 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)```
```# 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))

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

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)```
```#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 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)```
```# Definition of dictionary
'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))
```
```# 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 to access name of room
#x to access area in sqm
print('the ' + x + " is " + str(x) + " sqm")```
```using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
namespace StarPattern
{
class Program
{
static void Main(string[] args)
{
int x, y, z;
for (x =6; x >= 1; x--)
{
for (y = 1; y < x; y++)
{
Console.Write(" ");
}
for (z = 6; z >= x; z--)
{
Console.Write("*");
}
Console.WriteLine();
}
}
}
}```
star

Fri Nov 26 2021 10:42:38 GMT+0000 (UTC)

#list #forloop #for #loop #dicegame
star

Fri Nov 26 2021 10:06:16 GMT+0000 (UTC)

#list #forloop #for #loop ##dictionary
star

Fri Nov 26 2021 09:45:49 GMT+0000 (UTC)

#list #forloop #for #loop ##dictionary
star

Thu Nov 25 2021 17:01:18 GMT+0000 (UTC)

#list #forloop #for #loop ##dictionary
star

Thu Nov 25 2021 16:48:58 GMT+0000 (UTC)

#list #forloop #for #loop ##dictionary
star

Thu Nov 25 2021 16:42:08 GMT+0000 (UTC)

#list #forloop #for #loop ##dictionary
star

Thu Nov 25 2021 16:28:23 GMT+0000 (UTC)

#list #forloop #for #loop ##dictionary
star

Thu Nov 25 2021 16:16:28 GMT+0000 (UTC)

#list #forloop #for #loop ##dictionary
star

Thu Nov 12 2020 22:03:53 GMT+0000 (UTC) https://www.educba.com/patterns-in-c-sharp/

#c# #loops #for

#### Save snippets that work with our extensions   