Answer:
A social media sentiment analysis tells you how people feel about your brand online. Rather than a simple count of mentions or comments, sentiment analysis considers emotions and opinions. It involves collecting and analyzing information in the posts people share about your brand on social media.
Explanation:
Answer:
Use the default-information originate command.
Explanation:
A network administrator customizes a fixed path on a channel's edge router for creating any last recourse gateway that is the router connecting to the online / ISP. So, they using the usual command source to customize the edge router to connect this path instantly using RIP.
That's why the following answer is correct according to the scenario.
Answer: True
Explanation: Developers who design always take on idea that was created and add to it. Creating a piece of artwork/ Item.
Answer:
The VLookup checks a value inside a table through matching over the 1st column.
Lookup a value in a table by matching on the first column and the matched value is returned from the table.
Explanation:
The formula for the VLOOKUP is
=VLOOKUP (value to be matched in the first column, table from which the value will be looked for, col_index is the column inside the table from where the value is fetched, [range_lookup] is true for the approximate match, and false for exact match)
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.
