#!/usr/bin/env python
# coding: utf-8

# # Problem 1
# Import NumPy under the alias `np`.

# In[1]:


import numpy as np


# # Problem 2
# Import pandas under the alias `pd`.

# In[2]:


import pandas as pd


# # Problem 3
# Given the pandas Series `my_series`, generate a NumPy array that contains only the unique values from `my_series`. Assign this new array to a variable called `my_array`. Print `my_array` to ensure that the operation has been executed successfully.

# In[3]:


my_series = pd.Series([1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9])
my_series


# In[4]:


#Solution goes here
my_array = my_series.unique()
my_array


# # Problem 4
# Given the pandas DataFrame `my_data_frame`, generate a NumPy array that contains only the unique values from the second column. Assign this new array to a variable called `another_array`. Print `another_array` to ensure the operation has been executed successfully.

# In[5]:


my_data_frame = pd.DataFrame(np.random.randn(3,5))
my_data_frame


# In[6]:


#Solution goes here
another_array = my_data_frame[0].unique()
another_array


# # Problem 5
# Count the occurence of every element within the `my_series` variable that was created earlier in these practice problems.

# In[7]:


my_series.value_counts()


# # Problem 6
# Given the function `triple_digit`, apply this to every element within `my_series`.

# In[8]:


def triple_digit(x):
    return x + x*10 + x*100


# In[9]:


#Solution goes here
my_series.apply(triple_digit)


# # Problem 7
# Sort the `my_data_frame` variable that we created earlier based on the contents of its second column.

# In[10]:


my_data_frame.sort_values(0)