predicted_value1 = regr1.predict(9) meaning of this statement - python-2.7

Question

I am a newbe to the data science and I have downloaded the code which will tell the viewers for the next week.
But in this following code I am not able to understand the what the following function does, and how it will predict the values.
The data set is of 7 values for each. Why only 9 are inserted into the braces?
regr1 = linear_model.LinearRegression()
regr1.fit(x1, y1)
predicted_value1 = regr1.predict(9)
What thess lines will do?
Here is the full code:
import pandas as pd
def get_data(file_name):
data = pd.read_csv(file_name)
flash_x_parameter = []
flash_y_parameter = []
arrow_x_parameter = []
arrow_y_parameter = []
for x1,y1,x2,y2 in zip(data['flash_episode_number'],
data['flash_us_viewers'],
data['arrow_episode_number'],data['arrow_us_viewers']):
flash_x_parameter.append([float(x1)])
flash_y_parameter.append(float(y1))
arrow_x_parameter.append([float(x2)])
arrow_y_parameter.append(float(y2))
return flash_x_parameter,
flash_y_parameter,arrow_x_parameter,arrow_y_parameter
def more_viewers(x1,y1,x2,y2):
regr1 = linear_model.LinearRegression()
regr1.fit(x1, y1)
predicted_value1 = regr1.predict(9)
regr2 = linear_model.LinearRegression()
regr2.fit(x2, y2)
predicted_value2 = regr2.predict(9)
print predicted_value1,"are the flash viewers"
print predicted_value2,"are the arrow viewers"
if predicted_value1 > predicted_value2:
print "The Flash Tv Show will have more viewers for next week"
else:
print "Arrow Tv Show will have more viewers for next week"
x1,y1,x2,y2 = get_data('C:\\Users\\SHIVAPRASAD\\Desktop\\test.csv')
more_viewers(x1,y1,x2,y2)

Answer 1

No, your data is NOT the set of 7 values, it has 9 rows:
+----------------+-------------------+----------------+------------------+
| FLASH_EPISODE | FLASH_US_VIEWERS | ARROW_EPISODE | ARROW_US_VIEWERS |
+----------------+-------------------+----------------+------------------+
| 1 | 4.83 | 1 | 2.84 |
| 2 | 4.27 | 2 | 2.32 |
| 3 | 3.59 | 3 | 2.55 |
| 4 | 3.53 | 4 | 2.49 |
| 5 | 3.46 | 5 | 2.73 |
| 6 | 3.73 | 6 | 2.6 |
| 7 | 3.47 | 7 | 2.64 |
| 8 | 4.34 | 8 | 3.92 |
| 9 | 4.66 | 9 | 3.06 |
+----------------+-------------------+----------------+------------------+
(as your code is from Dataconomy Linear Regression Implementation in Python.)
So the value 9 in the command
predicted_value1 = regr1.predict(9)
is OK.