tidystep - 【Reading】Python for Data Analysis Part 1 Chapter 1-8

https://wesmckinney.com/book/python-basics.html

Python for Data Analysis by Wes Mckinney

{exec,command='bash'} echo 'hello'

1 Preliminaries

Essential Python Libraries: NumPy/pandas/matplotlib/SciPy/scikit-learn/statsmodels

install package

Code

import os
os.system('pip install numpy')
os.system('pip install matplotlib')
os.system('pip install pandas')
os.system('pip install seaborn')
os.system('pip install statsmodels')

Import Conventions

Code

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels as sm

2 Python Language Basics, IPython, and Jupyter Notebooks

Code

print("Hello world")

Hello world

Code

import numpy as np
data = [np.random.standard_normal() for i in range(7)]
data

[0.7810594371163568,
 -0.7334955362847929,
 0.5167768619691568,
 0.9043756983157261,
 -1.243885234931727,
 0.16636014847333624,
 1.1251843432345972]

An important characteristic of the Python language is the consistency of its object model. Every number, string, data structure, function, class, module, and so on exists in the Python interpreter in its own “box,” which is referred to as a Python object. Each object has an associated type (e.g., integer, string, or function) and internal data.

number and string

Code

v1=123
v2='abc'
type(v1)
type(v2)

str

list:

Code

a = [1, 2, 3]
a
a.append(4)
a
type(a)

list

Control Flow:f, elif, and else

Code

x = -5
if x < 0:
    print("It's negative")

It's negative

for loops

Code

sequence = [1, 2, None, 4, None, 5]
total = 0
for value in sequence:
    if value is None:
        continue
    total += value

while loops

Code

x = 256
total = 0
while x > 0:
    if total > 500:
        break
    total += x
    x = x // 2

pass

Code

if x < 0:
    print("negative!")
elif x == 0:
    # TODO: put something smart here
    pass
else:
    print("positive!")

positive!

range

Code

list(range(10))

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Code

list(range(0, 20, 2))

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

3 Built-In Data Structures, Functions, and Files

Tuple

Code

tup = (4, 5, 6)
tup

(4, 5, 6)

it start with 0 position

Code

tup[0]

a tuple of tuples:

Code

nested_tup = ((4, 5, 6), (7, 8))
nested_tup

((4, 5, 6), (7, 8))

!!!once the tuple is created it’s not possible to modify!!!

list

Code

a_list = [2, 3, 7, None]
a_list

[2, 3, 7, None]

Code

gen = range(10)
list(gen)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

append element at the end

Code

a_list.append("dwarf")
a_list

[2, 3, 7, None, 'dwarf']

insert element by position

Code

a_list.insert (1,"new")
a_list

[2, 'new', 3, 7, None, 'dwarf']

Dictionary

Set

Functions

Code

def function_without_return(x):
    print(x)
    
function_without_return('hello')

hello

Errors and Exception Handling

if error then return except

Code

def attempt_float(x):
    try:
        return float(x)
    except:
        return x

Code

attempt_float("1.2345")

1.2345

Code

attempt_float("something")

'something'

Files and the Operating System

4 NumPy Basics: Arrays and Vectorized Computation

Code

import numpy as np
data = np.array([[1.5, -0.1, 3], [0, -3, 6.5]])
data

array([[ 1.5, -0.1,  3. ],
       [ 0. , -3. ,  6.5]])

Code

data * 10

array([[ 15.,  -1.,  30.],
       [  0., -30.,  65.]])

Code

data.shape
data.dtype

dtype('float64')

5 Getting Started with pandas

Code

import numpy as np
import pandas as pd

Code

data = {"state": ["Ohio", "Ohio", "Ohio", "Nevada", "Nevada", "Nevada"],
        "year": [2000, 2001, 2002, 2001, 2002, 2003],
        "pop": [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
frame = pd.DataFrame(data)

frame

	state	year	pop
0	Ohio	2000	1.5
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2001	2.4
4	Nevada	2002	2.9
5	Nevada	2003	3.2

Code

frame.head()

	state	year	pop
0	Ohio	2000	1.5
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2001	2.4
4	Nevada	2002	2.9

Code

frame.tail()

	state	year	pop
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2001	2.4
4	Nevada	2002	2.9
5	Nevada	2003	3.2

Code

frame["state"]

0      Ohio
1      Ohio
2      Ohio
3    Nevada
4    Nevada
5    Nevada
Name: state, dtype: object

Code

frame.state

0      Ohio
1      Ohio
2      Ohio
3    Nevada
4    Nevada
5    Nevada
Name: state, dtype: object

6 Data Loading, Storage, and File Formats

7 Data Cleaning and Preparation

8 Data Wrangling: Join, Combine, and Reshape

Reference

https://wesmckinney.com/book/

--- title: "【Reading】Python for Data Analysis Part 1 Chapter 1-8" author: "Tony Duan" date: "2022-12-29" categories: [Book] execute: warning: false error: false format: html: toc: true code-fold: show code-tools: true --- https://wesmckinney.com/book/python-basics.html Python for Data Analysis by Wes Mckinney ![](images/image-1198507319.png) ```{exec,command='bash'} echo 'hello' ``` ## 1 Preliminaries Essential Python Libraries: NumPy/pandas/matplotlib/SciPy/scikit-learn/statsmodels **install package** ```{python} #| eval: false import os os.system('pip install numpy') os.system('pip install matplotlib') os.system('pip install pandas') os.system('pip install seaborn') os.system('pip install statsmodels') ``` **Import Conventions** ```{python} import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns import statsmodels as sm ``` ## 2 Python Language Basics, IPython, and Jupyter Notebooks ```{python} print("Hello world") ``` ```{python} import numpy as np data = [np.random.standard_normal() for i in range(7)] data ``` An important characteristic of the Python language is the consistency of its object model. Every number, string, data structure, function, class, module, and so on exists in the Python interpreter in its own "box," which is referred to as a Python object. Each object has an associated type (e.g., integer, string, or function) and internal data. ![](images/image-1357803323.png) number and string ```{python} v1=123 v2='abc' type(v1) type(v2) ``` list: ```{python} a = [1, 2, 3] a a.append(4) a type(a) ``` **Control Flow:f, elif, and else** ```{python} x = -5 if x < 0: print("It's negative") ``` **for loops** ```{python} sequence = [1, 2, None, 4, None, 5] total = 0 for value in sequence: if value is None: continue total += value ``` **while loops** ```{python} x = 256 total = 0 while x > 0: if total > 500: break total += x x = x // 2 ``` **pass** ```{python} if x < 0: print("negative!") elif x == 0: # TODO: put something smart here pass else: print("positive!") ``` **range** ```{python} list(range(10)) ``` ```{python} list(range(0, 20, 2)) ``` ## 3 Built-In Data Structures, Functions, and Files ### Tuple ```{python} tup = (4, 5, 6) tup ``` it start with 0 position ```{python} tup[0] ``` a tuple of tuples: ```{python} nested_tup = ((4, 5, 6), (7, 8)) nested_tup ``` !!!once the tuple is created it’s not possible to modify!!! ### list ```{python} a_list = [2, 3, 7, None] a_list ``` ```{python} gen = range(10) list(gen) ``` append element at the end ```{python} a_list.append("dwarf") a_list ``` insert element by position ```{python} a_list.insert (1,"new") a_list ``` ### Dictionary ### Set ### Functions ```{python} def function_without_return(x): print(x) function_without_return('hello') ``` ### Errors and Exception Handling if error then return except ```{python} def attempt_float(x): try: return float(x) except: return x ``` ```{python} attempt_float("1.2345") ``` ```{python} attempt_float("something") ``` ### Files and the Operating System ## 4 NumPy Basics: Arrays and Vectorized Computation ```{python} import numpy as np data = np.array([[1.5, -0.1, 3], [0, -3, 6.5]]) data ``` ```{python} data * 10 ``` ```{python} data.shape data.dtype ``` ## 5 Getting Started with pandas ```{python} import numpy as np import pandas as pd ``` ```{python} data = {"state": ["Ohio", "Ohio", "Ohio", "Nevada", "Nevada", "Nevada"], "year": [2000, 2001, 2002, 2001, 2002, 2003], "pop": [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]} frame = pd.DataFrame(data) frame ``` ```{python} frame.head() ``` ```{python} frame.tail() ``` ```{python} frame["state"] ``` ```{python} frame.state ``` ## 6 Data Loading, Storage, and File Formats ## 7 Data Cleaning and Preparation ## 8 Data Wrangling: Join, Combine, and Reshape ## Reference https://wesmckinney.com/book/