This is known as DNS poisoning, so called because it 'poisons' the entries of the DNS with false information.
Answer:
import pandas as pd
df = pd.read_csv('nycHistPop_csv', skiprows=5)
NY_region = list(df.columns[1: -1])
select_region = input("Enter region: ")_capitalize()
if select_region in NY_region:
pass
else:
raise("Region must be in New york")
def pop(location):
# or used describe() to replace all like: print(df[location]_describe())
print(df[location]_min())
print(df[location]_max())
print(df[location]_mean())
print(df[location]_median())
print(df[location]_td())
pop(select_region)
Explanation:
Pandas is a python packet that combines the power of numpy and matplotlib used for data manipulation. It is good for working with tables that are converted to dataframes.
The nycHistPop_csv file is read to the code to a dataframe called "df", then the user input and the function "pop" is used to automatically generate the statistics of a given region.
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