這篇文章主要給大家介紹了利用Python實(shí)現(xiàn)斐波那契數(shù)列的幾種方法，文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者使用Python具有一定的參考學(xué)習(xí)價值，需要的朋友們下面來一起學(xué)習(xí)學(xué)習(xí)吧

1.for循環(huán)

def fibonacci1(n):
    a, b = 0, 1
    for i in range(n):
        a, b = b, a+b
        print(a)


fibonacci1(3)

或

def fib1(w):
    a, b = 1, 1
    for i in range(w-1):
        a, b = b, a+b
    return a
???????
print(fib1(3))

[^1]剛好得出這個位置的數(shù)

2.while循環(huán)

def fibnaqi2(m):
    a, b = 0, 1
    i = 0
    while i < m:
        print(b)
        a, b = b, a+b
        i += 1

fibnaqi2(4)

[^1]剛好得出這個位置的數(shù)

3.使用遞歸

def fib2(q):
    if q == 1 or q == 2:
        return 1
    return fib2(q-1)+fib2(q-2)
???????
print(fib2(9))

4.遞歸+for循環(huán)

def fibnacci3(p):
    lst = []
    for i in range(p):
        if i == 1 or i == 0:
            lst.append(1)
        else:
            lst.append(lst[i-1]+lst[i-2])
    print(lst)

fibnacci3(5)

5.遞歸+while循環(huán)

def fibnacci4(k):
    lis = []
    i = 0
    while i<k:
        if i == 0 or i == 1:
            lis.append(1)
        else:
            lis.append(lis[i-2]+lis[i-1])
        i += 1
    print(lis)

fibnacci4(6)

6.遞歸+定義函數(shù)+for循環(huán)

def fibnacci5(o):
    def fn(i):
        if i < 2:
            return 1
        else:
            return (fn(i-2)+fn(i-1))
    for i in range(o):
        print(fn(i))
???????
fibnacci5(8)

7.指定列表

def fib3(e):
    if e == 1:
        return [1]
    if e == 2:
        return [1, 1]
    fibs = [1, 1]
    for i in range(2, e):
        fibs.append(fibs[-1]+fibs[-2])
    return fibs

print(fib3(12))

趣方程求解

題目描述

二次方程式 ax**2 + bx + c = 0 （a、b、c 用戶提供，為實(shí)數(shù)，a ≠ 0）

# 導(dǎo)入 cmath(復(fù)雜數(shù)學(xué)運(yùn)算) 模塊
import cmath

a = float(input('輸入 a: '))
b = float(input('輸入 b: '))
c = float(input('輸入 c: '))

# 計算
d = (b ** 2) - (4 * a * c)

# 兩種求解方式
sol1 = (-b - cmath.sqrt(d)) / (2 * a)
sol2 = (-b + cmath.sqrt(d)) / (2 * a)

print('結(jié)果為 {0} 和 {1}'.format(sol1, sol2))

pandas 每日一練

# -*- coding = utf-8 -*-
# @Time : 2022/7/26 21:48
# @Author : lxw_pro
# @File : pandas -8 練習(xí).py
# @Software : PyCharm

import pandas as pd
import numpy as np

df = pd.read_excel('text5.xlsx')
print(df)

print()

程序運(yùn)行結(jié)果如下：

Unnamed: 0 Unnamed: 0.1 project ... test_time date time
0 0 00:00:00 Python ... 2022-06-20 18:30:20 2022-06-20 18:30:20
1 1 1 Java ... 2022-06-18 19:40:20 2022-06-18 19:40:20
2 2 2 C ... 2022-06-08 13:33:20 2022-06-08 13:33:20
3 3 3 MySQL ... 2021-12-23 11:26:20 2021-12-23 11:26:20
4 4 4 Linux ... 2021-12-20 18:20:20 2021-12-20 18:20:20
5 5 5 Math ... 2022-07-20 16:30:20 2022-07-20 16:30:20
6 6 6 English ... 2022-06-23 15:30:20 2022-06-23 15:30:20
7 7 7 Python ... 2022-07-19 09:30:20 2022-07-19 09:30:20
[8 rows x 7 columns]

41、將test_time列設(shè)置為索引

print(df.set_index('test_time'))

???????print()

程序運(yùn)行結(jié)果如下：
Unnamed: 0 Unnamed: 0.1 ... date time
test_time ...
2022-06-20 18:30:20 0 00:00:00 ... 2022-06-20 18:30:20
2022-06-18 19:40:20 1 1 ... 2022-06-18 19:40:20
2022-06-08 13:33:20 2 2 ... 2022-06-08 13:33:20
2021-12-23 11:26:20 3 3 ... 2021-12-23 11:26:20
2021-12-20 18:20:20 4 4 ... 2021-12-20 18:20:20
2022-07-20 16:30:20 5 5 ... 2022-07-20 16:30:20
2022-06-23 15:30:20 6 6 ... 2022-06-23 15:30:20
2022-07-19 09:30:20 7 7 ... 2022-07-19 09:30:20
[8 rows x 6 columns]

42、生成一個和df長度相同的隨機(jī)數(shù)dataframe

df1 = pd.DataFrame(pd.Series(np.random.randint(1, 10, 8)))
print(df1)

???????print()

程序運(yùn)行結(jié)果如下：

0
0 1
1 3
2 2
3 7
4 7
5 3
6 5
7 1

43、將上一題生成的dataframe與df合并

df = pd.concat([df, df1], axis=1)
print(df)

???????print()

程序運(yùn)行結(jié)果如下：

Unnamed: 0 Unnamed: 0.1 project ... date time 0
0 0 00:00:00 Python ... 2022-06-20 18:30:20 1
1 1 1 Java ... 2022-06-18 19:40:20 3
2 2 2 C ... 2022-06-08 13:33:20 2
3 3 3 MySQL ... 2021-12-23 11:26:20 7
4 4 4 Linux ... 2021-12-20 18:20:20 7
5 5 5 Math ... 2022-07-20 16:30:20 3
6 6 6 English ... 2022-06-23 15:30:20 5
7 7 7 Python ... 2022-07-19 09:30:20 1
[8 rows x 8 columns]

44、生成新的一列new為popularity列減去之前生成隨機(jī)數(shù)列

df['new'] = df['popularity'] - df[0]
print(df)

???????print()

程序運(yùn)行結(jié)果如下：

Unnamed: 0 Unnamed: 0.1 project popularity ... date time 0 new
0 0 00:00:00 Python 95 ... 2022-06-20 18:30:20 1 94
1 1 1 Java 92 ... 2022-06-18 19:40:20 3 89
2 2 2 C 145 ... 2022-06-08 13:33:20 2 143
3 3 3 MySQL 141 ... 2021-12-23 11:26:20 7 134
4 4 4 Linux 84 ... 2021-12-20 18:20:20 7 77
5 5 5 Math 148 ... 2022-07-20 16:30:20 3 145
6 6 6 English 146 ... 2022-06-23 15:30:20 5 141
7 7 7 Python 149 ... 2022-07-19 09:30:20 1 148
[8 rows x 9 columns]

45、檢查數(shù)據(jù)中是否含有任何缺失值

jch = df.isnull().values.any()
print(jch)    # 運(yùn)行結(jié)果為：False

???????print()

46、將popularity列類型轉(zhuǎn)換為浮點(diǎn)數(shù)

fds = df['popularity'].astype(np.float64)
print(fds)

???????print()

程序運(yùn)行結(jié)果如下：

0 95.0
1 92.0
2 145.0
3 141.0
4 84.0
5 148.0
6 146.0
7 149.0
Name: popularity, dtype: float64

47、計算popularity大于100的次數(shù)

cs = len(df[df['popularity'] > 100])
print(cs)    # 運(yùn)行結(jié)果為：5

???????print()

48、查看project列共有幾種學(xué)歷

ckj = df['project'].nunique()
print(ckj)    # 運(yùn)行結(jié)果為：7

???????print()

49、查看每科出現(xiàn)的次數(shù)

ckc = df.project.value_counts()
print(ckc)

print()

程序運(yùn)行結(jié)果如下：

Python 2
Java 1
C 1
MySQL 1
Linux 1
Math 1
English 1
Name: project, dtype: int64

50、提取popularity與new列的和大于136的最后3行

df1 = df[['popularity', 'new']]
hh = df1.apply(np.sum, axis=1)
res = df.iloc[np.where(hh > 136)[0][-3:], :]
print(res)

程序運(yùn)行結(jié)果如下：

Unnamed: 0 Unnamed: 0.1 project popularity ... date time 0 new
5 5 5 Math 148 ... 2022-07-20 16:30:20 3 145
6 6 6 English 146 ... 2022-06-23 15:30:20 5 141
7 7 7 Python 149 ... 2022-07-19 09:30:20 1 148
[3 rows x 9 columns]

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