Lawrence-Krukrubo

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

Lawrence-Krukrubo

import seaborn as sns

titanic_df = sns.load_dataset('titanic')
titanic_df.head()

Lawrence-Krukrubo

# First, let's import numpy.
import numpy as np

# Next, let's save the titanic_df we loaded before, as titanic.csv file.
titanic_df.to_csv('titanic.csv', index=False)

# Next, let's load titanic.csv as a numpy array,
titanic_arr = np.loadtxt('titanic.csv', delimiter = ',', skiprows=1, usecols=[0,1,4])  

# I passed the following parameters:-

Lawrence-Krukrubo

# Let's wrap the import in a try-catch block

try:
    data = np.genfromtxt('titanic.csv', delimiter=',', names=True, dtype=None, encoding='utf8')
except Exception as e:
    print(type(e))

# Let's see the first five rows of the data ID array

data[:5]

Lawrence-Krukrubo

import numpy as np
import pandas as pd

# First let's save the link to the excel file
battle_link = 'https://assets.datacamp.com/production/repositories/487/datasets/5e8897e4624f8577ed0d33aeafbe7bd88bfc424b/battledeath.xlsx'

# Next let's load the file into an io.excel.base file
xls = pd.ExcelFile(battle_link)
# Let's see its type
print(type(xls))

Lawrence-Krukrubo

import pandas as pd
import pickle

# Open pickle file and load data:
with open('ratings.pickle', 'rb') as f:
    ratings_data = pickle.load(f)
 
# Print ratings_data
print(ratings_data)
 

Lawrence-Krukrubo