Write a function that returns the quotient of two numbers with proper error handling.
Find all the values whose income is larger than 500 in the given dataset.
```
dictionary = {"Name" :["Alex","Bob","Cathy","Don","Emma","Frank"],
"Age" :[15,40,30,60,19,25],
"income" :[300,800,1000,850,100,250]}
``` Does your answer scale for TB and PB-sized datasets (i.e. big data processing and analytics)?
Tags: #ai #python #rstats #linux #datascience #tech #opensource
Make a series from the Age column in the given dataset.
```
dictionary = {"Name" :["Alex","Bob","Cathy","Don","Emma","Frank"],
"Age" :[15,40,30,60,19,25],
"income" :[300,800,1000,850,100,250]}
```
Check the sum of the income column for the given dataset.
```
dictionary = {"Name" :["Alex","Bob","Cathy","Don","Emma","Frank"],
"Age" :[15,40,30,60,19,25],
"income" :[300,800,1000,850,100,250]}
```
Think about how your answer would scale for 100, 90001, or 10E6 entries.
Tags: #ai #python #linux #rstats #datascience
Write a function that takes strings as input and concatenates them with error handling.
#data_question
Create two DataFrames satisfying these conditions: one must be a subset of the other and contains only the last few rows.
```
dictionary = {"Name" :["Alex","Bob","Cathy","Don","Emma","Frank"],
"Age" :[15,40,30,60,19,25],
"income" :[300,800,1000,850,100,250]}
```
Convert the following ndarray into a DataFrame object
```
import numpy as np
foo = np.array([[1,2,3],[4,5,6]])
```
Tags: #ai #datascience #tech #linux #python
Given
```
import pandas as pd
this_dict = {'Ohio':35000, 'Texas':71000, 'Oregon':16000, 'Utah':5000}
baz = pd.Series(this_dict)
```
What is the output?
```
baz.index = ['the', 'quick', 'brown', 'fox']
```
#data_question
Use `normal` to generate normally distributed steps with some mean and standard deviation.
hint: edit your `steps` filter from the preceding question, with 5000 walks at 1000 steps each, to resemble this array
```
steps = np.random.normal(loc = 0, scale = 0.25, size = (nwalks, nsteps))
```