Intro To Pandas

Question 1

What is Pandas

Answer 1

One of the most common python libraries used by data scientists.

Question 2

Why is pandas so popular?

Answer 2

Because it can connect to just about any data source, such as SQL database, from web, load from excel and much more

Question 3

How is pandas used?

Answer 3

Pandas provide easy-to-use data structures and tools for effectively loading, manipulating and exporting in-memory data in python

Question 4

Why do data manipulation with pandas?

Answer 4

The pandas library helps you explore your data and visually see the structure of your output as you are transforming your data.

Question 5

What dataframes work very nicely with python machine learning libraries?

Answer 5

Sickit-learn, statsmodels and data visualization libraries( matplotib, seaborn)

Question 6

How does having data cleaned in a dataframe help?

Answer 6

Let’s you quickly visualize your data or feed it into a machine learning algorithm

Question 7

What is tabular?

Answer 7

Data presented in columns or tables

Question 8

What is Slicing?

Answer 8

To access just certain parts of our dataset

Question 9

How to use slicing?

Answer 9

With square bracketz, single brackets return a panda series and double brackets return dataframe

Question 10

Difference between a NumPy array and a pandas Series?

Answer 10

The essential difference is how they are indexed

Question 11

How to mount data?

Answer 11

from google.colab import drive

drive.mount(‘/content/drive’)

Question 12

How to filter mortgage names from you data?

Answer 12

df[ ‘Mortgage Name’ ]

Question 13

How to filter valuable mortgage name data?

Answer 13

df[’ Mortgage Name’ ].value_counts()

Question 14

How to filter out just 30 year mortgages?

Answer 14

df[df[ ‘Mortgage Name’ ] == ‘30 Year’s]

Question 15

How to combine filters?

Answer 15

df = df.loc[mortgage_filter & interest_filter, :]

df

Question 16

What operator negates a filter?

Answer 16

~

Question 17

How to rename columns?

Answer 17

df = df.rename(columns={‘Starting Balance’: ‘starting_balance’})

Question 18

How to delete columns?

Answer 18

df = df.drop(columns=[‘new_balnce’])
df.head()
Or del df[‘starting_balance’]

Question 19

How to check for duplicate rows?

Answer 19

df.duplicated() to check for duplicates

And to count them use df.duplicated().sum()

Question 20

How to remove duplicate rows?

Answer 20

df - df.drop_duplicates()

df.duplicated().sum()

Question 21

How to drop columns

Answer 21

df = df.drop(columns=[‘unamed: 0’, ‘passengerId’])

Question 22

What is df.nunique()?

Answer 22

Tells us how many unique values are in each room, finding relationships between the column value and other data

Question 23

What is df.info()?

Answer 23

Tells us a lot about our data. Checks columns, rows, data types, and missing values

Question 24

How to replace a value or change into a number and type?

Answer 24

df.[‘sibsp’] = df[‘sibsp’].replace(‘one, 1)
df[‘sibs’] = df[‘sibsp’].astype(int)
df.info()

Question 25

What codes are missing values?

Answer 25

NaN, Na or Null

Question 26

What is the function used to identify missing data?

Answer 26

df.isna() and df.isna().sum() to returns nulls for each column

Question 27

When should you just drop the rows that are missing?

Answer 27

If only a small percentage of rows are missing data, you may just want to drop them. There is no hard and fast rule, but one rule of thumb is if fewer than 2% of your rows are missing data, might be good to drop em