Explanation:
You can retrieve a column in a pandas DataFrame object by using the DataFrame object name, followed by the label of the column name in brackets.
So if the DataFrame object name is dataframe1 and the column we are trying to retrieve the 'X' column, then we retrieve the column using the statement, dataframe1['X'].
Here's a simple Python script that imports pandas and uses a data frame:
import pandas as pd
data = [['Alex',10],['Bob',12],['Clarke',13]]
df = pd.DataFrame(data,columns=['Name','Age'],dtype=float)
print (df)
When run, this script returns:
Name Age
0 Alex 10.0
1 Bob 12.0
2 Clarke 13.0
Explanation:
Reference: http://www.learningaboutelectronics.com/Articles/How-to-retrieve-a-column-from-a-pandas-dataframe-object-in-Python.php

Explanation:
Use FIELDTERMINATOR ='\t' for tab.
Use ROWTERMINATOR ='\0x0A ' for UNIX-style line endings
Use FIRSTROW= 2 for a header row
Note: Using Row Terminators
The row terminator can be the same character as the terminator for the last field. Generally, however, a distinct row terminator is useful. For example, to produce tabular output, terminate the last field in each row with the newline character (\n) and all other fields with the tab character (\t).
If you want to output a line feed character only (LF) as the row terminator - as is typical on Unix and Linux computers - use hexadecimal notation to specify the LF row terminator.
For example:
bcp -r '0x0A'
FIRSTROW
FIRSTROW =first_row Specifies the number of the first row to load. The default is 1. This indicates the first row in the specified data file. The row numbers are determined by counting the row terminators. FIRSTROW is 1-based.
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/import-export/specify-field-and-row-terminators-sql-server
https://docs.microsoft.com/en-us/sql/t-sql/functions/openrowset-transact-sql

Explanation:
Most time is spent in the category Other - time required by the visual for preparing queries, waiting for other visuals to complete, or performing other background processing.
Note: Each visual's log information includes the time spent (duration) to complete the following categories of tasks:
DAX query - if a DAX query was required, this is the time between the visual sending the query, and for Analysis Services to return the results.
Visual display - time required for the visual to draw on the screen, including time required to retrieve any web images or geocoding.
Other - time required by the visual for preparing queries, waiting for other visuals to complete, or performing other background processing.
Reference: https://docs.microsoft.com/en-us/power-bi/create-reports/desktop-performance-analyzer

Explanation:
Users indicate that the data in Power BI reports is stale. You discover that the refresh process of the Power BI model occasionally times out.
With Query Diagnostics, you can achieve a better understanding of what Power Query is doing at authoring and at refresh time in Power BI Desktop. While we'll be expanding on this feature in the future, including adding the ability to use it during full refreshes, at this time you can use it to understand what sort of queries you're emitting, what slowdowns you might run into during authoring refresh, and what kind of background events are happening.
Reference: https://docs.microsoft.com/en-us/power-query/querydiagnostics

Explanation:
Themes, contrast and colorblind-friendly colors.
You should ensure that your reports have enough contrast between text and any background colors.
Certain color combinations are particularly difficult for users with color vision deficiencies to distinguish.
These include the following combinations:
green and red
green and brown
blue and purple
green and blue
light green and yellow
blue and grey
green and grey
green and black
Avoid using these colors together in a chart, or on the same report page.
Reference: https://docs.microsoft.com/en-us/power-bi/create-reports/desktop-accessibility-creating-reports