This repository contains python & machine learning materials, solutions. These courses has taken from the datacamp
NumPy arrays are a bit like Python lists, but still very much different at the same time.Here all the elements should be same type(Homogeneous elements)
List doesn't support arithmetic manipulations like double the list, *,%,/..etc. so Numpy came into existence
Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python. Sounds promising! The DataFrame is one of Pandas' most important data structures. It's basically a way to store tabular data where you can label the rows and the columns. One way to build a DataFrame is from a dictionary.
Numpy supports only for homogeneous elements.So pandas came, it can have heterogeneous elements.
Putting data in a dictionary and then building a DataFrame works, but it's not very efficient. What if you're dealing with millions of observations? In those cases, the data is typically available as files with a regular structure. One of those file types is the CSV file, which is short for "comma-separated values".
- Column Access: dataframe[["column_name"]]
- Row Access:
- loc-labled access dataframe.loc[["index_name/row name"]]
- iloc-index access dataframe.iloc[["index_number"]]
Note: np.logical_and(), np.logical_or() and np.logical_not(), the Numpy variants of the and, or and not operators
- Dictionary:
for key, val in my_dict.items()
- Numpy array:(2d or more dimensional array)
for val in np.nditer(my_array)
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123)
all_walks = []
for i in range(10) :
random_walk = [0]
for x in range(100) :
step = random_walk[-1]
dice = np.random.randint(1,7)
if dice <= 2:
step = max(0, step - 1)
elif dice <= 5:
step = step + 1
else:
step = step + np.random.randint(1,7)
random_walk.append(step)
all_walks.append(random_walk)
# Convert all_walks to Numpy array: np_aw
np_aw = np.array(all_walks)
# Plot np_aw and show
plt.plot(np_aw)
plt.show()
# Clear the figure
plt.clf()
# Transpose np_aw: np_aw_t
np_aw_t = np.transpose(np_aw)
# Plot np_aw_t and show
plt.plot(np_aw_t)
plt.show()# Create a list of strings: spells
spells = ["protego", "accio", "expecto patronum", "legilimens"]
# Use map() to apply a lambda function over spells: shout_spells
shout_spells = map(lambda spell: spell + '!!!', spells)
# Convert shout_spells to a list: shout_spells_list
shout_spells_list = list(shout_spells)
# Convert shout_spells into a list and print it
print(shout_spells_list)# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'pippin', 'aragorn', 'boromir', 'legolas', 'gimli', 'gandalf']
# Use filter() to apply a lambda function over fellowship: result
result = filter(lambda member : len(member) > 6, fellowship)
# Convert result to a list: result_list
result_list = list(result)
# Convert result into a list and print it
print(result_list)he reduce() function is useful for performing some computation on a list and, unlike map() and filter(), returns a single value as a result. To use reduce(), you must import it from the functools module.
# Import reduce from functools
from functools import reduce
# Create a list of strings: stark
stark = ['robb', 'sansa', 'arya', 'brandon', 'rickon']
# Use reduce() to apply a lambda function over stark: result
result = reduce(lambda item1, item2 : item1 + item2, stark)
# Print the result
print(result)In [1]: avengers = ['hawkeye', 'iron man', 'thor', 'quicksilver']
In [2]: names = ['barton', 'stark', 'odinson', 'maximoff']
In [3]: z = zip(avengers, names)
In [4]: print(type(z)) <class 'zip'>
In [5]: z_list = list(z)
In [6]: print(z_list) [('hawkeye', 'barton'), ('iron man', 'stark'), ('thor', 'odinson'), ('quicksilver', 'maximoff')# Define plot_pop()
def plot_pop(filename, country_code):
# Initialize reader object: urb_pop_reader
urb_pop_reader = pd.read_csv(filename, chunksize=1000)
# Initialize empty DataFrame: data
data = pd.DataFrame()
# Iterate over each DataFrame chunk
for df_urb_pop in urb_pop_reader:
# Check out specific country: df_pop_ceb
df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == country_code]
# Zip DataFrame columns of interest: pops
pops = zip(df_pop_ceb['Total Population'],
df_pop_ceb['Urban population (% of total)'])
# Turn zip object into list: pops_list
pops_list = list(pops)
# Use list comprehension to create new DataFrame column 'Total Urban Population'
df_pop_ceb['Total Urban Population'] = [int(tup[0] * tup[1]) for tup in pops_list]
# Append DataFrame chunk to data: data
data = data.append(df_pop_ceb)
# Plot urban population data
data.plot(kind='scatter', x='Year', y='Total Urban Population')
plt.show()
# Set the filename: fn
fn = 'ind_pop_data.csv'
# Call plot_pop for country code 'CEB'
plot_pop('ind_pop_data.csv','CEB')
# Call plot_pop for country code 'ARB'
plot_pop('ind_pop_data.csv','ARB')
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