import pandas as pd

data = {
    'Name': ['John', 'Jane', 'Mike', 'Emily', 'Tom', 'Alex', 'Sara'],
    'Age': [25, 31, 45, 19, 27, 33, 28],
    'City': ['New York', 'London', 'Paris', 'Tokyo', 'Sydney', 'New York', 'New York'],
    'Salary': [50000, 75000, 90000, 40000, 60000, 80000, 65000]
}

df = pd.DataFrame(data)

# Print column as list
df['Name']

0     John
1     Jane
2     Mike
3    Emily
4      Tom
5     Alex
6     Sara
Name: Name, dtype: object

# Print column as dataframe
df[['Name']]

	Name
0	John
1	Jane
2	Mike
3	Emily
4	Tom
5	Alex
6	Sara

# Print column as dataframe
df[['Name', 'City']]

	Name	City
0	John	New York
1	Jane	London
2	Mike	Paris
3	Emily	Tokyo
4	Tom	Sydney
5	Alex	New York
6	Sara	New York

# Get all unique entries in column
df['City'].unique()

array(['New York', 'London', 'Paris', 'Tokyo', 'Sydney'], dtype=object)

# Get how much a single entrie exists in the dataframe
# df[['City']].value_counts() # value_counts of dataframe
df['City'].value_counts() # value_counts of specific column

New York    3
London      1
Paris       1
Tokyo       1
Sydney      1
Name: City, dtype: int64

# Binary filter on string column
df[(df['City'].str.contains('New York'))]

	Name	Age	City	Salary
0	John	25	New York	50000
5	Alex	33	New York	80000
6	Sara	28	New York	65000

# Binary filter on int column
df[(df['Salary'] >= 70000)]

	Name	Age	City	Salary
1	Jane	31	London	75000
2	Mike	45	Paris	90000
5	Alex	33	New York	80000

# Binary filter on multiple int column
df[(df['Salary'] >= 70000) & (df['Age'] < 35)]

	Name	Age	City	Salary
1	Jane	31	London	75000
5	Alex	33	New York	80000

# Binary filter to get all records where Salary is not null or NaN
df[(df['Salary'].notnull())]

	Name	Age	City	Salary
0	John	25	New York	50000
1	Jane	31	London	75000
2	Mike	45	Paris	90000
3	Emily	19	Tokyo	40000
4	Tom	27	Sydney	60000
5	Alex	33	New York	80000
6	Sara	28	New York	65000