code in python and explain Step 1: Import data. skiprows: This parameter is use to skip passed rows in new data frame df1 = pd.read_csv(my.csv, skiprows = 1) df2 = pd.read_csv(my.csv, skiprows = [0, 2, 3]) index_col: This is to allow you to set which columns to be used as the index of the dataframe. The default value is None, and pandas will add a new column start from 0 to specify the index column. sep: a shortened name for separator. This operator is the delimiter used in our dataset or in Laymans term, how the data items are separated in our CSV file. users = pd.read_csv('users.csv', sep='|', index_col='user_id') In our data, columns are separated by | and we want to user user_id as index column Step 2: Getting and Knowing your Data (1%) Show the first 10 entries show the last 10 entries Print the name and data type of each column? Print only the occupation column How many different occupations are in this dataset? What is the most frequently appeared occupation? What is the mean age of users? What is the occupation with least occurrence? Step 3: Filtering and Sorting Data (1%) Show only people are older than 40 years old sort by zip_code of the user who is/are the oldest person/people in the data frame Step 4: Grouping (1%) What is the mean age per occupation (round to 2 decimal places) For each occupation, calculate the minimum and maximum ages For each occupation present the percentage of women and men.

1. code in python and explain
Step 1: Import data.
skiprows: This parameter is use to skip passed rows in new data frame
df1 = pd.read_csv(my.csv, skiprows = 1) df2 = pd.read_csv(my.csv, skiprows = [0, 2, 3])
index_col: This is to allow you to set which columns to be used as the index of the dataframe.
The default value is None, and pandas will add a new column start from 0 to specify the index
column.
sep: a shortened name for separator. This operator is the delimiter used in our dataset or in
Laymans term, how the data items are separated in our CSV file.
users = pd.read_csv('users.csv', sep='|', index_col='user_id')
In our data, columns are separated by | and we want to user user_id as index column
Step 2: Getting and Knowing your Data (1%)
Show the first 10 entries
show the last 10 entries
Print the name and data type of each column?
Print only the occupation column
How many different occupations are in this dataset?
What is the most frequently appeared occupation?
What is the mean age of users?
What is the occupation with least occurrence?
Step 3: Filtering and Sorting Data (1%)
Show only people are older than 40 years old
sort by zip_code of the user
who is/are the oldest person/people in the data frame
Step 4: Grouping (1%)
What is the mean age per occupation (round to 2 decimal places)
For each occupation, calculate the minimum and maximum ages
For each occupation present the percentage of women and men