目錄
- 1.for循環(huán)
- 2.while循環(huán)
- 3.使用遞歸
- 4.遞歸+for循環(huán)
- 5.遞歸+while循環(huán)
- 6.遞歸+定義函數(shù)+for循環(huán)
- 7.指定列表
- 趣方程求解
- pandas 每日一練
斐波那契數(shù)列——經(jīng)典例子，永不過時！！！
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]剛好得出這個位置得數(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 += 1fibnaqi2(4)
[^1]剛好得出這個位置得數(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 fibsprint(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 cmatha = float(input('輸入 a: '))b = float(input('輸入 b: '))c = float(input('輸入 c: '))# 計(jì)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 : PyCharmimport pandas as pdimport numpy as npdf = 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長度相同得隨機(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、將上一題生成得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、生成新得一列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(jì)算popularity大于100得次數(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)得次數(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列得和大于136得最后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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