Data visualization - "Netflix Stock Profile"

[byte5016296434]

Hello there i did the “Visualize Data with Python” Course.
This is my final Project, where i look into the Netflix Stock in the year 2017.
The presentation is not made with love.

yeah, can someone tell me how to upload things in this forum?

Does anyone know why in the last plot the x-axis looks that postponed?
And why i cant upload a .pdf and .py file? Didnt they say i have to save it that way and upload it?

Have great day.

byte5016296434!

"Introduction
In this project, you will act as a data visualization developer at Yahoo Finance! You will be helping the “Netflix Stock Profile” team visualize the Netflix stock data. In finance, a stock profile is a series of studies, visualizations, and analyses that dive into different aspects a publicly traded company’s data.
For the purposes of the project, you will only visualize data for the year of 2017. Specifically, you will be in charge of creating the following visualizations:

• The distribution of the stock prices for the past year
• Netflix’s earnings and revenue in the last four quarters
• The actual vs. estimated earnings per share for the four quarters in 2017
• A comparison of the Netflix Stock price vs the Dow Jones Industrial Average price in 2017
  Note: We are using the Dow Jones Industrial Average to compare the Netflix stock to the larter stock market. Learn more about why the Dow Jones Industrial Average is a general reflection of the larger stock market here.
  During this project, you will analyze, prepare, and plot data. Your visualizations will help the financial analysts asses the risk of the Netflix stock.
  After you complete your visualizations, you’ll be creating a presentation to share the images with the rest of the Netflix Stock Profile team. Your slides should include:
• A title slide
• A list of your visualizations and your role in their creation for the “Stock Profile” team
• A visualization of the distribution of the stock prices for Netflix in 2017
• A visualization and a summary of Netflix stock and revenue for the past four quarters and a summary
• A visualization and a brief summary of their earned versus actual earnings per share
• A visualization of Netflix stock against the Dow Jones stock (to get a sense of the market) in 2017
  Financial Data Source: Yahoo Finance
  Step 1
  Let’s get our notebook ready for visualizing! Import the modules that you’ll be using in this project:
• from matplotlib import pyplot as plt
• import pandas as pd
• import seaborn as sns
  In [11]:

from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
Step 2
Let’s load the datasets and inspect them.
Load NFLX.csv into a DataFrame called netflix_stocks. Then, quickly inspect the DataFrame using print().
Hint: Use the pd.read_csv()function).
Note: In the Yahoo Data, Adj Close represents the adjusted close price adjusted for both dividends and splits. This means this is the true closing stock price for a given business day.
In [12]:

netflix_stocks=pd.read_csv(“NFLX.csv”)
print(netflix_stocks)
Date Open High Low Close Adj Close
0 2017-01-01 124.959999 143.460007 124.309998 140.710007 140.710007
1 2017-02-01 141.199997 145.949997 139.050003 142.130005 142.130005
2 2017-03-01 142.839996 148.289993 138.259995 147.809998 147.809998
3 2017-04-01 146.699997 153.520004 138.660004 152.199997 152.199997
4 2017-05-01 151.910004 164.750000 151.610001 163.070007 163.070007
5 2017-06-01 163.520004 166.869995 147.300003 149.410004 149.410004
6 2017-07-01 149.800003 191.500000 144.250000 181.660004 181.660004
7 2017-08-01 182.490005 184.619995 164.229996 174.710007 174.710007
8 2017-09-01 175.550003 189.949997 172.440002 181.350006 181.350006
9 2017-10-01 182.110001 204.380005 176.580002 196.429993 196.429993
10 2017-11-01 197.240005 202.479996 184.320007 195.509995 195.509995
11 2017-12-01 186.990005 194.490005 178.380005 191.960007 191.960007

   Volume  

0 181772200
1 91432000
2 110692700
3 149769200
4 116795800
5 135675800
6 185144700
7 136523100
8 111427900
9 208657800
10 161719700
11 115103700
Load DJI.csv into a DataFrame called dowjones_stocks. Then, quickly inspect the DataFrame using print().
Note: You can learn more about why the Dow Jones Industrial Average is a industry reflection of the larger stock market here.
In [13]:

dowjones_stocks=pd.read_csv(“DJI.csv”)
print(dowjones_stocks)
Date Open High Low Close
0 2017-01-01 19872.859375 20125.580078 19677.939453 19864.089844
1 2017-02-01 19923.810547 20851.330078 19831.089844 20812.240234
2 2017-03-01 20957.289063 21169.109375 20412.800781 20663.220703
3 2017-04-01 20665.169922 21070.900391 20379.550781 20940.509766
4 2017-05-01 20962.730469 21112.320313 20553.449219 21008.650391
5 2017-06-01 21030.550781 21535.029297 20994.220703 21349.630859
6 2017-07-01 21392.300781 21929.800781 21279.300781 21891.119141
7 2017-08-01 21961.419922 22179.109375 21600.339844 21948.099609
8 2017-09-01 21981.769531 22419.509766 21709.630859 22405.089844
9 2017-10-01 22423.470703 23485.250000 22416.000000 23377.240234
10 2017-11-01 23442.900391 24327.820313 23242.750000 24272.349609
11 2017-12-01 24305.400391 24876.070313 23921.900391 24719.220703

   Adj Close      Volume  

0 19864.089844 6482450000
1 20812.240234 6185580000
2 20663.220703 6941970000
3 20940.509766 5392630000
4 21008.650391 6613570000
5 21349.630859 7214590000
6 21891.119141 5569720000
7 21948.099609 6150060000
8 22405.089844 6342130000
9 23377.240234 7302910000
10 24272.349609 7335640000
11 24719.220703 6589890000
Load NFLX_daily_by_quarter.csv into a DataFrame called netflix_stocks_quarterly. Then, quickly inspect the DataFrame using print().
In [14]:

netflix_stocks_quarterly=pd.read_csv(“NFLX_daily_by_quarter.csv”)
print(netflix_stocks_quarterly)
Date Open High Low Close Adj Close
0 2017-01-03 124.959999 128.190002 124.309998 127.489998 127.489998
1 2017-01-04 127.489998 130.169998 126.550003 129.410004 129.410004
2 2017-01-05 129.220001 132.750000 128.899994 131.809998 131.809998
3 2017-01-06 132.080002 133.880005 129.809998 131.070007 131.070007
4 2017-01-09 131.479996 131.990005 129.889999 130.949997 130.949997
… … … … … … …
246 2017-12-22 188.330002 190.949997 186.800003 189.940002 189.940002
247 2017-12-26 189.779999 189.940002 186.399994 187.759995 187.759995
248 2017-12-27 187.800003 188.100006 185.220001 186.240005 186.240005
249 2017-12-28 187.179993 194.490005 186.850006 192.710007 192.710007
250 2017-12-29 192.509995 193.949997 191.220001 191.960007 191.960007

   Volume Quarter  

0 9437900 Q1
1 7843600 Q1
2 10185500 Q1
3 10657900 Q1
4 5766900 Q1
… … …
246 3878900 Q4
247 3045700 Q4
248 4002100 Q4
249 10107400 Q4
250 5187600 Q4

[251 rows x 8 columns]
Step 3
Let’s learn more about our data. The datasets are large and it may be easier to view the entire dataset locally on your computer. Open the CSV files directly from the folder you downloaded for this project.

• NFLX is the stock ticker symbol for Netflix and ^DJI is the stock ticker symbol for the Dow Jones industrial Average, which is why the CSV files are named accordingly
• In the Yahoo Data, Adj Close is documented as adjusted close price adjusted for both dividends and splits.
• You can learn more about why the Dow Jones Industrial Average is a industry reflection of the larger stock market here.
  Answer the following questions by inspecting the data in the NFLX.csv,DJI.csv, and NFLX_daily_by_quarter.csv in your computer.
  What year is represented in the data? Look out for the latest and earliest date.
  In [15]:

2017

• Is the data represented by days, weeks, or months?
• In which ways are the files different?
• What’s different about the columns for netflix_stocks versus netflix_stocks_quarterly?
  In [16]:

year 2017

nflx = 1st of Month, Monthly for 1 year

nflx = daily for 1 year without weekends

?
?
Step 4
Great! Now that we have spent sometime looking at the data, let’s look at the column names of the DataFrame netflix_stocks using .head().
In [17]:

netflix_stocks.head()
Out[17]:

Date
Open
High
Low
Close
Adj Close
Volume
0
2017-01-01
124.959999
143.460007
124.309998
140.710007
140.710007
181772200
1
2017-02-01
141.199997
145.949997
139.050003
142.130005
142.130005
91432000
2
2017-03-01
142.839996
148.289993
138.259995
147.809998
147.809998
110692700
3
2017-04-01
146.699997
153.520004
138.660004
152.199997
152.199997
149769200
4
2017-05-01
151.910004
164.750000
151.610001
163.070007
163.070007
116795800
What do you notice? The first two column names are one word each, and the only one that is not is Adj Close!
The term Adj Close is a confusing term if you don’t read the Yahoo Documentation. In Yahoo, Adj Close is documented as adjusted close price adjusted for both dividends and splits.
This means this is the column with the true closing price, so these data are very important.
Use Pandas to change the name of of the column to Adj Close to Price so that it is easier to work with the data. Remember to use inplace=True.
Do this for the Dow Jones and Netflix Quarterly pandas dataframes as well. Hint: Use .rename()).
In [18]:

netflix_stocks.rename(columns={
‘Adj Close’: ‘Price’},
inplace=True)
?
print(netflix_stocks)
?
netflix_stocks_quarterly.rename(columns={
‘Adj Close’: ‘Price’},
inplace=True)
?
print(netflix_stocks_quarterly)
?
dowjones_stocks.rename(columns={
‘Adj Close’: ‘Price’},
inplace=True)
?
print(dowjones_stocks)
?
Date Open High Low Close Price
0 2017-01-01 124.959999 143.460007 124.309998 140.710007 140.710007
1 2017-02-01 141.199997 145.949997 139.050003 142.130005 142.130005
2 2017-03-01 142.839996 148.289993 138.259995 147.809998 147.809998
3 2017-04-01 146.699997 153.520004 138.660004 152.199997 152.199997
4 2017-05-01 151.910004 164.750000 151.610001 163.070007 163.070007
5 2017-06-01 163.520004 166.869995 147.300003 149.410004 149.410004
6 2017-07-01 149.800003 191.500000 144.250000 181.660004 181.660004
7 2017-08-01 182.490005 184.619995 164.229996 174.710007 174.710007
8 2017-09-01 175.550003 189.949997 172.440002 181.350006 181.350006
9 2017-10-01 182.110001 204.380005 176.580002 196.429993 196.429993
10 2017-11-01 197.240005 202.479996 184.320007 195.509995 195.509995
11 2017-12-01 186.990005 194.490005 178.380005 191.960007 191.960007

   Volume  

0 181772200
1 91432000
2 110692700
3 149769200
4 116795800
5 135675800
6 185144700
7 136523100
8 111427900
9 208657800
10 161719700
11 115103700
Date Open High Low Close Price
0 2017-01-03 124.959999 128.190002 124.309998 127.489998 127.489998
1 2017-01-04 127.489998 130.169998 126.550003 129.410004 129.410004
2 2017-01-05 129.220001 132.750000 128.899994 131.809998 131.809998
3 2017-01-06 132.080002 133.880005 129.809998 131.070007 131.070007
4 2017-01-09 131.479996 131.990005 129.889999 130.949997 130.949997
… … … … … … …
246 2017-12-22 188.330002 190.949997 186.800003 189.940002 189.940002
247 2017-12-26 189.779999 189.940002 186.399994 187.759995 187.759995
248 2017-12-27 187.800003 188.100006 185.220001 186.240005 186.240005
249 2017-12-28 187.179993 194.490005 186.850006 192.710007 192.710007
250 2017-12-29 192.509995 193.949997 191.220001 191.960007 191.960007

   Volume Quarter  

0 9437900 Q1
1 7843600 Q1
2 10185500 Q1
3 10657900 Q1
4 5766900 Q1
… … …
246 3878900 Q4
247 3045700 Q4
248 4002100 Q4
249 10107400 Q4
250 5187600 Q4

[251 rows x 8 columns]
Date Open High Low Close
0 2017-01-01 19872.859375 20125.580078 19677.939453 19864.089844
1 2017-02-01 19923.810547 20851.330078 19831.089844 20812.240234
2 2017-03-01 20957.289063 21169.109375 20412.800781 20663.220703
3 2017-04-01 20665.169922 21070.900391 20379.550781 20940.509766
4 2017-05-01 20962.730469 21112.320313 20553.449219 21008.650391
5 2017-06-01 21030.550781 21535.029297 20994.220703 21349.630859
6 2017-07-01 21392.300781 21929.800781 21279.300781 21891.119141
7 2017-08-01 21961.419922 22179.109375 21600.339844 21948.099609
8 2017-09-01 21981.769531 22419.509766 21709.630859 22405.089844
9 2017-10-01 22423.470703 23485.250000 22416.000000 23377.240234
10 2017-11-01 23442.900391 24327.820313 23242.750000 24272.349609
11 2017-12-01 24305.400391 24876.070313 23921.900391 24719.220703

       Price      Volume  

0 19864.089844 6482450000
1 20812.240234 6185580000
2 20663.220703 6941970000
3 20940.509766 5392630000
4 21008.650391 6613570000
5 21349.630859 7214590000
6 21891.119141 5569720000
7 21948.099609 6150060000
8 22405.089844 6342130000
9 23377.240234 7302910000
10 24272.349609 7335640000
11 24719.220703 6589890000
Run netflix_stocks.head() again to check your column name has changed.
In [19]:

netflix_stocks.head()
Out[19]:

Date
Open
High
Low
Close
Price
Volume
0
2017-01-01
124.959999
143.460007
124.309998
140.710007
140.710007
181772200
1
2017-02-01
141.199997
145.949997
139.050003
142.130005
142.130005
91432000
2
2017-03-01
142.839996
148.289993
138.259995
147.809998
147.809998
110692700
3
2017-04-01
146.699997
153.520004
138.660004
152.199997
152.199997
149769200
4
2017-05-01
151.910004
164.750000
151.610001
163.070007
163.070007
116795800
Call .head() on the DataFrame dowjones_stocks and netflix_stocks_quarterly.
In [20]:

print(dowjones_stocks.head())
print(netflix_stocks_quarterly.head())
Date Open High Low Close
0 2017-01-01 19872.859375 20125.580078 19677.939453 19864.089844
1 2017-02-01 19923.810547 20851.330078 19831.089844 20812.240234
2 2017-03-01 20957.289063 21169.109375 20412.800781 20663.220703
3 2017-04-01 20665.169922 21070.900391 20379.550781 20940.509766
4 2017-05-01 20962.730469 21112.320313 20553.449219 21008.650391

      Price      Volume  

0 19864.089844 6482450000
1 20812.240234 6185580000
2 20663.220703 6941970000
3 20940.509766 5392630000
4 21008.650391 6613570000
Date Open High Low Close Price
0 2017-01-03 124.959999 128.190002 124.309998 127.489998 127.489998
1 2017-01-04 127.489998 130.169998 126.550003 129.410004 129.410004
2 2017-01-05 129.220001 132.750000 128.899994 131.809998 131.809998
3 2017-01-06 132.080002 133.880005 129.809998 131.070007 131.070007
4 2017-01-09 131.479996 131.990005 129.889999 130.949997 130.949997

 Volume Quarter  

0 9437900 Q1
1 7843600 Q1
2 10185500 Q1
3 10657900 Q1
4 5766900 Q1
Step 5
In this step, we will be visualizing the Netflix quarterly data!
We want to get an understanding of the distribution of the Netflix quarterly stock prices for 2017. Specifically, we want to see in which quarter stock prices flucutated the most. We can accomplish this using a violin plot with four violins, one for each business quarter!

1. Start by creating a variable ax and setting it equal to sns.violinplot(). This will instantiate a figure and give us access to the axes through the variable name ax.
2. Use sns.violinplot() and pass in the following arguments:

• The Quarter column as the x values
• The Price column as your y values
• The netflix_stocks_quarterly dataframe as your data

3. Improve the readability of the chart by adding a title of the plot. Add “Distribution of 2017 Netflix Stock Prices by Quarter” by using ax.set_title()
4. Change your ylabel to “Closing Stock Price”
5. Change your xlabel to “Business Quarters in 2017”
6. Be sure to show your plot!
   In [26]:

sns.set_context(“notebook”)
sns.set_style(“whitegrid”)
#plt.figsize=(15, 10)
ax=sns.violinplot(data = netflix_stocks_quarterly, x = “Quarter”, y = “Price”)
ax.set_title(“Distribution of 2017 Netflix Stock Prices by Quarter”)
plt.xlabel(“Business Quarters in 2017”)
plt.ylabel(“Closing Stock Price”)
plt.savefig(“violin_netflix_stock_quarterly.png”)
plt.show()

Graph Literacy

• What are your first impressions looking at the visualized data?
• In what range(s) did most of the prices fall throughout the year?
• What were the highest and lowest prices?
  Step 6
  Next, we will chart the performance of the earnings per share (EPS) by graphing the estimate Yahoo projected for the Quarter compared to the actual earnings for that quarters. We will accomplish this using a scatter chart.

1. Plot the actual EPS by using x_positions and earnings_actual with the plt.scatter() function. Assign red as the color.
2. Plot the actual EPS by using x_positions and earnings_estimate with the plt.scatter() function. Assign blue as the color
3. Often, estimates and actual EPS are the same. To account for this, be sure to set your transparency alpha=0.5 to allow for visibility pf overlapping datapoint.
4. Add a legend by using plt.legend() and passing in a list with two strings [“Actual”, “Estimate”]
5. Change the x_ticks label to reflect each quarter by using plt.xticks(x_positions, chart_labels)
6. Assing "“Earnings Per Share in Cents” as the title of your plot.
   In [65]:

from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
?
x_positions = [1, 2, 3, 4]
chart_labels = [“1Q2017”,“2Q2017”,“3Q2017”,“4Q2017”]
earnings_actual =[.4, .15,.29,.41]
earnings_estimate = [.37,.15,.32,.41 ]
?
ax=plt.subplot()
sns.set_context(“notebook”)
sns.set_style(“whitegrid”)
plt.xticks(x_positions, chart_labels)
plt.scatter(x_positions,earnings_actual,color=“red”,alpha=0.5)
plt.scatter(x_positions,earnings_estimate,color=“blue”,alpha=0.5)
plt.legend([“Actual”, “Estimate”])
plt.title(“Earnings Per Share in Cents”)
plt.savefig(“EPS_Quater”)
plt.show()

Graph Literacy

• What do the purple dots tell us about the actual and estimate earnings per share in this graph? Hint: In color theory red and blue mix to make purple.
  Step 7

Next, we will visualize the earnings and revenue reported by Netflix by mapping two bars side-by-side. We have visualized a similar chart in the second Matplotlib lesson Exercise 4.
?
As you may recall, plotting side-by-side bars in Matplotlib requires computing the width of each bar before hand. We have pasted the starter code for that exercise below.
?

1. Fill in the n, t, d, w values for the revenue bars
2. Plot the revenue bars by calling plt.bar() with the newly computed x_values and the revenue_by_quarter data
3. Fill in the n, t, d, w values for the earnings bars
4. Plot the revenue bars by calling plt.bar() with the newly computed x_values and the earnings_by_quarter data
5. Create a legend for your bar chart with the labels provided
6. Add a descriptive title for your chart with plt.title()
7. Add labels to each quarter by assigning the position of the ticks through the code provided. Hint: plt.xticks(middle_x, quarter_labels)
8. Be sure to show your plot!
   ?
   In [68]:

The metrics below are in billions of dollars

?
revenue_by_quarter = [2.79, 2.98,3.29,3.7]
earnings_by_quarter = [.0656,.12959,.18552,.29012]
quarter_labels = [“2Q2017”,“3Q2017”,“4Q2017”, “1Q2018”]
sns.set_style(“whitegrid”)
ax=plt.subplot()

Revenue

n = 1 # This is our first dataset (out of 2)
t = 2 # Number of dataset
d = 4 # Number of sets of bars
w = 1 # Width of each bar
bars1_x = [telement + wn for element
in range(d)]
print(bars1_x)
plt.bar(bars1_x,revenue_by_quarter)

Earnings

n = 2 # This is our second dataset (out of 2)
t = 2 # Number of dataset
d = 4 # Number of sets of bars
w = 1 # Width of each bar
bars2_x = [telement + wn for element
in range(d)]
print(bars2_x)
plt.bar(bars2_x,earnings_by_quarter)
plt.legend([“Revenue”, “Earnings”])
middle_x = [ (a + b) / 2.0 for a, b in zip(bars1_x, bars2_x)]
labels = [“Revenue”, “Earnings”]
plt.xticks(middle_x, quarter_labels)
plt.title(“Earnings and revenue reported by Netflix”)
plt.savefig(“Earnings and revenue reported by Netflix.png”)
plt.show()
cal=(0.29012*100)/3.7
print(cal)
?
[1, 3, 5, 7]
[2, 4, 6, 8]

7.8410810810810805
Graph Literacy
What are your first impressions looking at the visualized data?

• Does Revenue follow a trend?
• Do Earnings follow a trend?
• Roughly, what percentage of the revenue constitutes earnings?
  In [69]:

indeed, goes up, percentage should be calculated between the blue bars and compared

yes, goes up like the revenue maybe there`s a correlation

7.8410810810810805 % for the last quater

Step 8

?
In this last step, we will compare Netflix stock to the Dow Jones Industrial Average in 2017. We will accomplish this by plotting two line charts side by side in one figure.
?
Since Price which is the most relevant data is in the Y axis, let’s map our subplots to align vertically side by side.

• We have set up the code for you on line 1 in the cell below. Complete the figure by passing the following arguments to plt.subplots() for the first plot, and tweaking the third argument for the second plot
  • 1– the number of rows for the subplots
  • 2 – the number of columns for the subplots
  • 1 – the subplot you are modifying
    ?
• Chart the Netflix Stock Prices in the left-hand subplot. Using your data frame, access the Date and Price charts as the x and y axes respectively. Hint: (netflix_stocks['Date'], netflix_stocks['Price'])
• Assign “Netflix” as a title to this subplot. Hint: ax1.set_title()
• For each subplot, set_xlabel to "Date" and set_ylabel to "Stock Price"
• Chart the Dow Jones Stock Prices in the left-hand subplot. Using your data frame, access the Date and Price charts as the x and y axes respectively. Hint: (dowjones_stocks['Date'], dowjones_stocks['Price'])
• Assign “Dow Jones” as a title to this subplot. Hint: plt.set_title()
• There is some crowding in the Y axis labels, add some space by calling plt.subplots_adjust(wspace=.5)
• Be sure to .show() your plots.
  ?
  In [104]:

Left plot Netflix

ax1 = plt.subplot(1, 2, 1)
plt.plot(netflix_stocks[‘Date’],netflix_stocks[‘Price’])
ax1.set_title(“Netflix”)
plt.xlabel(“Date”)
plt.ylabel(“Stock Price”)
print(len(netflix_stocks[‘Date’]))
ax1.set_xticks([1,2,3,4,5,6,7,8,9,10,11,12])
ax1.set_xticklabels([“1”,“2”,“3”,“4”,“5”,“6”,“7”,“8”,“9”,“10”,“11”,“12”])
?

Right plot Dow Jones

ax2 = plt.subplot(1, 2, 2)
plt.plot(dowjones_stocks[‘Date’],dowjones_stocks[‘Price’])
plt.subplots_adjust(wspace=.5)
ax2.set_title(“DowJones”)
plt.xlabel(“Date”)
plt.ylabel(“Stock Price”)
ax2.set_xticks([1,2,3,4,5,6,7,8,9,10,11,12])
ax2.set_xticklabels([“1”,“2”,“3”,“4”,“5”,“6”,“7”,“8”,“9”,“10”,“11”,“12”])
?
plt.savefig(“Netflix_DowJones.png”)
plt.show()
12

• How did Netflix perform relative to Dow Jones Industrial Average in 2017?
• Which was more volatile?
• How do the prices of the stocks compare?
  Step 9
  It’s time to make your presentation! Save each of your visualizations as a png file with plt.savefig(“filename.png”).
  As you prepare your slides, think about the answers to the graph literacy questions. Embed your observations in the narrative of your slideshow!
  Remember that your slideshow must include:
• A title slide
• A list of your visualizations and your role in their creation for the “Stock Profile” team
• A visualization of the distribution of the stock prices for Netflix in 2017
• A visualization and a summary of Netflix stock and revenue for the past four quarters and a summary
• A visualization and a brief summary of their earned versus actual earnings per share
• A visualization of Netflix stock against the Dow Jones stock (to get a sense of the market) in 2017

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