サンプルコード集

CODE: 0-1 (if文)

wake_up_time = 12

if ( wake_up_time==7 ):
    print("朝ごはんを食べる")
elif ( wake_up_time==12 ):
    print("昼ごはんを食べる")
else:
    print("何も食べない")

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CODE: 0-2 (if文)

wake_up_time = 7

is_hungry = True

if ( wake_up_time==7 ):
    if ( is_hungry==True ):
        print("朝ごはんを食べる")
    else:
        print("朝ごはんを食べない")
else:
    print("何も食べない")

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CODE: 0-3 (if文)

wake_up_time = 8
if ( wake_up_time == 7 ):
    print("たべる")
    print("おなかいっぱい")

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CODE: 0-4 (if文)

wake_up_time = 8
if ( wake_up_time == 7 ):
    print("たべる")
print("おなかいっぱい")

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CODE: 1-1 (for文)

for i in range(0, 3):
    print(i)

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CODE: 1-2 (for文)

a = 3
for i in range(0, 10):
    a += 4
print(a)

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CODE: 1-3 (for文)

s = 0
for i in range(3, 12):
    s += i
print(s)

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CODE: 1-4 (for文)

for i in range(0, 5):
    print(i)
    print("Hello!")

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CODE: 1-5 (for文)

s = 0

for i in range(0, 5):
    s += i * 2

print(s)

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CODE: 1-6 (for文)

a = 1

for i in range(1, 6):
    a = a * i

print(a)

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CODE: 2-1 (関数)

def print_name(name):
    print("私の名前は" + name + "です。")
    return

print_name("田中太郎")

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CODE: 2-2 (関数)

def calc_mean(a, b, c):
    m = (a + b + c) / 3
    return(m)


m = calc_mean(3, 6, 9)
print(m)

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CODE: 2-3 (関数)

def print_hello():
    print("hello!")
    return


print_hello()

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CODE: 2-4 (関数, for文)

def calc3(x, y, z):
    a = (x * y * z) / 3
    return(a)

for i in range(0, 4):
    x = calc3(i, 3, 6)
    print(x)

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CODE: 2-5 (関数, for文)

def calc3(x, y, z):
    a = (x * y * z) / 3
    return(a)

x = 0
for i in range(0, 4):
    x += calc3(i, 3, 6)

print(x)

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CODE: 2-6 (関数, for文)

def calc3(x, y, z):
    a = (x * y * z) / 3
    return(a)

x = 0
for i in range(0, 5):
    x += calc3(i, 3, 6)

if ( x<100 ):
    print("OK")
else:
    print("NG")

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CODE: 3-1 (配列)

a = [3, 4, 9, 2]
print(a[0])
print(a[3])

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CODE: 3-2 (配列)

a = [3, 5, 9, 0]
a[2] = 4
print(a)

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CODE: 3-3 (配列)

a = []
a.append(4)
a.append(2)
a.append(8)
a.pop(2)

print(a)

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CODE: 3-4 (配列, 関数)

def calc3(x, y, z):
    a = (x * y * z) / 3
    return(a)

a = [3, 5, 9, 10]
x = 0
for i in range(0, 4):
    b = a[i]
    x += calc3(b, 3, 6)

print(x)

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CODE: 3-5 (配列, 関数)

def calc_tax(a):
    r = 1.1
    x = a * r
    return(x)

goods = [1000, 2000, 3000]
total = 0

for i in range(0, len(goods)):
    total += goods[i]

total = calc_tax(total)
print(int(total))

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CODE: 3-6 (配列, 関数)

def calc_tax(a):
    r = 1.1
    x = a * r
    return(x)

goods = []
goods.append(1000)
goods.append(2000)
goods.append(3000)
goods.append(4000)
total = 0

for i in range(0, len(goods)):
    total += calc_tax(goods[i])

print(int(total))

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CODE: 4-1 (関数, for文)

def calc_pow(x, n):
    y = 1
    for i in range(0, n):
        y = y * x
    return(y)


x = 4
n = 3
y = calc_pow(x, n)

print(y)

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CODE: 4-2 (関数, for文)

def calc_pow(x, n):
    y = 1
    for i in range(0, n):
        y = y * x
    return(y)


x = 2
n = 5
y = calc_pow(x, n)

if ( y<40 ):
    print("OK")
else:
    print("NG")

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CODE: 4-3 (配列, 関数)

def calc_pow(x, n):
    y = 1
    for i in range(0, n):
        y = y * x
    return(y)

x = 2
list0 = []

for i in range(0, 4):
    y = calc_pow(x, i)
    list0.append(y)

s = sum(list0)
print(s)

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CODE: 5-1 (関数, if文)

def inch2cm(inch):
    cm = inch*2.54
    return(cm)


def main():
    inch = 15
    cm = inch2cm(inch)
    print(cm)
    return(0)

main()

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CODE: 5-2 (関数)

def inch2cm(inch):
    cm = inch*2.54
    return(cm)


def feet2cm(feet):
    cm = feet*30.48
    return(cm)


def main():
    inch = 15
    feet = 3
    cm = inch2cm(inch) + feet2cm(feet)
    print(cm)
    return(0)

main()

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CODE: 5-3 (関数, if文)

def inch2cm(inch):
    cm = inch*2.54
    return(cm)


def feet2cm(feet):
    cm = feet*30.48
    return(cm)


def main():
    inch = 10
    feet = 2
    cm = inch2cm(inch) + feet2cm(feet)

    if ( cm>90 ):
        print("OK")
    else:
        print("NG")
    return(0)

main()

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CODE: 6-1 (openpyxl, 配列)

import openpyxl as px

OUTPUT_FILE = "6-1_test.xlsx"

def main():
    wb = px.Workbook()
    ws = wb.active

    ws.cell(column=1, row=1).value = 3
    ws.cell(column=2, row=1).value = 5
    ws.cell(column=1, row=2).value = 9
    ws.cell(column=2, row=2).value = 2

    wb.save(OUTPUT_FILE)

main()

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CODE: 6-2 (openpyxl, 関数)

import openpyxl as px

OUTPUT_FILE = "6-2_test.xlsx"

def main():
    wb = px.Workbook()
    ws = wb.active

    for i in range(1, 10):
        for j in range(1, 10):
            ws.cell(column=i, row=j).value = i * j

    wb.save(OUTPUT_FILE)


main()
    

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CODE: 6-3 (openpyxl, 関数)

import openpyxl as px

INPUT_FILE = "6-2_test.xlsx"

def main():
    wb = px.load_workbook(INPUT_FILE)
    ws = wb.active

    cell25 = ws.cell(column=2, row=5)
    print(cell25)

    cell38 = ws.cell(column=3, row=8)
    print(cell38)

    return(0)



main()

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CODE: 7-1 (関数)

def print_myinfo(first, last, age):
    print("私の名前は%s%sです。" % (first, last))
    print("私の年齢は%d歳です。" % (age))
    return

firstname = "山田"
lastname = "太郎"
age = 39

print_myinfo(firstname, lastname, age)

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CODE: 7-2 (for文)

for i in range(0, 4):
    print("%04d.jpg" % (i))

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CODE: 7-3 (関数, if文)

def jpg2png(jpgname):
    if ( not(".jpg" in jpgname) ):
        print("Error: 拡張子が.jpgではありません")
        return("")
    else:
        pngname = jpgname.replace(".jpg", ".png")
        return(pngname)

fname = "sample.jpg"
pngname = jpg2png(fname)
print(pngname)

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CODE: 7-4 (正規表現, 関数)

import re

def main():
    text = "名前：山田太郎、年齢：30歳、職業：エンジニア"
    pattern = r'名前：(.*)、年齢：(.*)歳、職業：(.*)'

    dates = re.findall(pattern, text)
    for name, age, job in dates:
        print("%s, %s, %s" % (name, age, job))
    return(0)


main()

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CODE: 7-5 (正規表現, 関数)

import re

def main():
    text = "イベントは2023年5月15日から2023年5月17日まで開催されます。"
    pattern = r'(\d{4})年(\d{1,2})月(\d{1,2})日'

    dates = re.findall(pattern, text)
    for year, month, day in dates:
        print("%04d/%02d/%02d" % (int(year), int(month), int(day)))
    return(0)


main()

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CODE: 7-6 (正規表現, 関数)

import re

def main():
    text = "イベントは2023年5月15日から2023年5月17日まで開催されます。"
    pattern = r'(\d{4})年'

    text_sub = re.sub(pattern, "2025年", text)
    print(text_sub)
    return(0)


main()

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CODE: 8-1 (ファイル入出力, 関数)

# ---- Config ----

FILE_NAME = "8-1_output.txt"


def main():
    s = "40 92 20 44 91"

    fname = FILE_NAME
    f = open(fname, mode="w")
    f.write(s)
    f.close()

    return(0)


main()

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CODE: 8-2 (ファイル入出力, 関数)

# ---- Config ----

FILE_NAME = "8-1_output.txt"


def main():
    fname = FILE_NAME
    f = open(fname, mode="r")
    s = f.read()
    f.close()
    s_list = s.split(" ")
    print(s_list)

    return(0)


main()

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CODE: 9-1 (ファイル入出力, 配列)

import random

# ---- Config ----

FILE_NAME = "9-1_output.txt"
ROWS = 3
COLUMNS = 5


def make_rand_table(rows, cols):
    datalist = []
    for i in range(0, rows):
        d = []
        for j in range(0, cols):
            d.append(str(random.randint(1, 100)))
        datalist.append(d)

    return(datalist)


def write_file(datalist, fname):

    table_str = ""

    rows = len(datalist)
    for i in range(0, rows):
        table_str += " ".join(datalist[i]) + "\n"

    f = open(fname, mode="w")
    f.write(table_str)
    f.close()

    return(0)


def main():
    rows = ROWS
    cols = COLUMNS
    datalist = make_rand_table(rows, cols)
    print(datalist)

    fname = FILE_NAME
    write_file(datalist, fname)

    return(0)


main()

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CODE: 9-2 (ファイル入出力, 配列)

# ---- Config ----

FILE_NAME = "9-1_output.txt"


def read_file(fname):
    f  = open(fname, mode="r")
    table_str = f.read()
    f.close()

    datalist = []

    lines = table_str.split("\n")
    rows = len(lines)
    for i in range(0, rows):
        if ( lines[i]=="" ):
            continue
        d = lines[i].split(" ")
        datalist.append(d)

    return(datalist)


def main():
    fname = FILE_NAME

    datalist = read_file(fname)
    print(datalist)

    return(0)


main()

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CODE: a-1 (Numpy, 配列)

import numpy as np


def main():
    a_arr = np.array([4, 6, 3, 1, 9])
    print(a_arr)
    print(a_arr.ndim)   # 次元
    print(a_arr.shape)  # 大きさ

    b_arr = np.zeros((4, 3))    # 4行3列の行列を作成(0埋め)
    print(b_arr)
    print(b_arr.ndim)   # 次元
    print(b_arr.shape)  # 大きさ

    c_arr = np.zeros((5, 8))    # 5行8列の行列を作成(0埋め)
    print(c_arr)        
    print(c_arr.ndim)   # 次元
    print(c_arr.shape)  # 大きさ

    return(0)


main()

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CODE: a-2 (Numpy, 配列)

import numpy as np


def main():
    arr = np.array([4, 3, 5, 2, 9])
    print(np.min(arr))      # 最小値
    print(np.max(arr))      # 最大値
    print(np.mean(arr))     # 平均値
    print(np.std(arr))      # 標準偏差
    print(np.var(arr))      # 分散
    print(np.sum(arr))      # 合計値
    print(np.sort(arr))     # 並び替え

    # 九九の表を作成
    arr99 = np.zeros((9, 9))
    for i in range(0, 9):
        for j in range(0, 9):
            arr99[i][j] = (i+1) * (j+1)

    print(arr99)

    return(0)


main()

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CODE: a-3 (Matplotlib, Numpy)

import numpy as np
import matplotlib.pyplot as plt


# ---- Config ----

FILE_NAME = "a-3_output.png"


def main():
    fname = FILE_NAME

    x_arr = np.linspace(0, 10, 1000)
    y_arr = np.sin(x_arr)

    plt.plot(x_arr, y_arr)
    plt.savefig(fname)

    return(0)


main()

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CODE: a-4 (Matplotlib, Numpy)

import numpy as np
import matplotlib.pyplot as plt


# ---- Config ----

FILE_NAME = "a-4_output.png"


# y = ax^3 + bx^2 + cx + d
def cubic_equation(a, b, c, d, x):
    y = a * x**3 + b * x**2 + c * x + d
    return(y)


def main():
    a = 1
    b = -5
    c = -2
    d = 5
    fname = FILE_NAME

    x_arr = np.linspace(-10, 10, 1000)
    y_arr = cubic_equation(a, b, c, d, x_arr)

    plt.plot(x_arr, y_arr)
    plt.savefig(fname)

    return(0)


main()

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CODE: b-1 (Pandas, Numpy)

import pandas as pd


# ---- Config ----

FILE_NAME = "http://python.hisakatano.com/data/weight1.xlsx"


def main():
    fname = FILE_NAME
    df = pd.read_excel(fname, index_col=0, header=0)
    print("読み込んだデータ:")
    print(df)
    print()

    # "一日目"の列を抜き出す
    col1 = df[1]
    print("一日目の列:")
    print(col1)
    print()

    # "体重[kg]"の行を抜き出す
    weight_row = df[0:1]
    print("体重[kg]の行:")
    print(weight_row)
    print()

    # numpy arrayへ変換
    weight_arr = weight_row.to_numpy()
    print("numpy arrayへ変換された体重[kg]の行:")
    print(weight_arr)
    return(0)


main()

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CODE: b-2 (Pandas, Matplotlib)

import pandas as pd
import matplotlib.pyplot as plt


# ---- Config ----

FILE_NAME = "http://python.hisakatano.com/data/weight1.xlsx"
OUTPUT_FILE = "output_b-2.png"


def main():
    fname = FILE_NAME
    df = pd.read_excel(fname, index_col=0, header=0)

    # headerを抜き出す
    header = df.columns
    header_arr = header.to_numpy()
    print("header:")
    print(header_arr)
    print()


    # "歩数"の行を抜き出す
    hosu_row = df[1:2]
    print("歩数の行:")
    hosu_arr = hosu_row.to_numpy()[0]
    print(hosu_arr)

    output_file = OUTPUT_FILE
    plt.plot(header_arr, hosu_arr)
    plt.savefig(output_file)

    return(0)


main()

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CODE: c-1 (Biopython, ファイル入出力)

from Bio import SeqIO

# ---- Config ----

#FILE_NAME = "https://www.uniprot.org/uniprot/P59082.fasta"
FILE_NAME = "P59082.fasta"


def main():
    fname = FILE_NAME
    with open(fname, mode="r") as f:
        for record in SeqIO.parse(f, "fasta"):
            id_part = record.id
            desc_part = record.description
            seq = record.seq

            print("id: %s" % (id_part))
            print("desc: %s" % (desc_part))
            print("seq: %s" % (seq))

    return(0)


main()

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CODE: c-2 (Biopython, ファイル入出力)

from Bio import SeqIO

# ---- Config ----

FILE_NAME = "P59082.fasta"


def calc_gc(seq):
    gc = (seq.count("G") + seq.count("C")) / len(seq)
    return(gc)


def main():
    fname = FILE_NAME
    with open(fname, mode="r") as f:
        for record in SeqIO.parse(f, "fasta"):
            id_part = record.id
            desc_part = record.description
            seq = record.seq
            rev_seq = seq.reverse_complement()
            gc = calc_gc(seq)

            print("id: %s" % (id_part))
            print("desc: %s" % (desc_part))
            print("Reverse complement seq: %s" % (rev_seq))
            print("GC: %s" % (gc))

    return(0)


main()

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CODE: c-3 (Biopython, ファイル入出力)

from Bio import SeqIO
import gzip

# ---- Config ----

FILE_NAME = "pdb_seqres.txt.gz"
PRINT_NUM = 5


def main():
    fname = FILE_NAME

    f = gzip.open(fname, mode="rt")

    i = 0
    for record in SeqIO.parse(f, "fasta"):
        id_part = record.id
        desc_part = record.description
        seq = record.seq

        print("id: %s" % (id_part))
        print("desc: %s" % (desc_part))
        print("seq: %s" % (seq))

        i += 1
        if ( PRINT_NUM<=i ):
            break

    f.close()

    return(0)


main()

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CODE: d-1 (scikit-learn, Pandas)

from sklearn.decomposition import PCA
import pandas as pd
import matplotlib.pyplot as plt


# ---- Config ----

CSV_FILE = "http://python.hisakatano.com/data/iris.csv"
OUTPUT_FILE_1 = "d-1_output_1.png"
OUTPUT_FILE_2 = "d-1_output_2.png"


# ---------------------------------------------------------
# PCAの次元圧縮結果のプロット
# ---------------------------------------------------------

def plot_pca_result(df, output_file):
    plt.clf()
    label_type = set(df["label"])
    for t in label_type:
        df2 = df.query('label=="%s"' % (t))
        plt.scatter(x=df2[0], y=df2[1], label=t)
    plt.legend()
    plt.grid()
    plt.xlabel("PC1")
    plt.ylabel("PC2")
    plt.savefig(output_file)
    return


# ---------------------------------------------------------
# メイン
# ---------------------------------------------------------

def main():
    # ---- (1) data frameでのデータ取得 ----
    df = pd.read_csv(CSV_FILE, index_col=None, header=0)
    print(df)

    # ---- (2) numpyに変換しデータ成形 ----
    x = df.iloc[:, 0:4].to_numpy() 

    # ---- (3) scikit-learnによる分析処理 ----
    pca = PCA(n_components=2)
    pca.fit(x)
    x0 = pca.transform(x)

    # ---- 次元圧縮結果の可視化 ----
    df2 = pd.DataFrame(x0)
    df2["label"] = df["species"]
    print(df2)
    plot_pca_result(df2, OUTPUT_FILE_1)

    # ---- 寄与度の可視化 ----
    df3 = pd.DataFrame(pca.components_.T)
    df3["label"] = df.columns[0:4]
    print(df3)
    plot_pca_result(df3, OUTPUT_FILE_2)

    return(0)


main()

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CODE: d-2 (scikit-learn, Pandas)

from sklearn.linear_model import LinearRegression
import pandas as pd
import matplotlib.pyplot as plt


# ---- Config ----

CSV_FILE = "http://python.hisakatano.com/data/linear_regression_data.csv"
OUTPUT_FILE = "d-2_output.png"


# ---------------------------------------------------------
# メイン処理
# ---------------------------------------------------------

def main():
    # ---- (1) data frameでのデータ取得 ----
    df = pd.read_csv(CSV_FILE, index_col=None, header=0)
    print(df)

    # ---- (2) numpyに変換しデータ成形 ----
    data_na = df.to_numpy() 

    # ---- (3) scikit-learnによる分析処理 ----
    lr = LinearRegression()
    x = data_na[:, 0]
    y = data_na[:, 1]

    x = x.reshape(len(x), 1)    # xは行列にする。
                                # 今回は2次元データなのでNx1の行列。
    print(x)
    print(y)
    lr.fit(x, y)

    coef = lr.coef_[0]          # 傾き
    intercept = lr.intercept_   # 切片
    print("傾き: %f" % (coef))
    print("切片: %f" % (intercept))

    plt.scatter(x, y, color="red")
    plt.plot(x, lr.predict(x), color="blue")

    plt.grid()
    plt.savefig(OUTPUT_FILE)

    return(0)


main()

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CODE: d-3 (scikit-learn, Pandas)

from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import numpy as np
import warnings


# ---- Config ----

warnings.simplefilter("ignore")     # 警告が表示されないようにする
CSV_FILE = "http://python.hisakatano.com/data/iris.csv"



# ---------------------------------------------------------
# MLPの学習
# ---------------------------------------------------------

def learn_mlp(x_arr, y_arr):
    print("Learning ... ")
    model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=100, activation="relu", verbose=True)
    model.fit(x_arr, y_arr)
    print("Learning ... done")

    return(model)


# ---------------------------------------------------------
# メイン処理
# ---------------------------------------------------------

def main():
    # ---- (1) data frameでのデータ取得 ----
    df = pd.read_csv(CSV_FILE, index_col=None, header=0)
    print(df)

    x_df = df.iloc[:, 0:4]
    y_df = df.iloc[:, 4]

    # ---- (2) numpy arrayに変換しデータ成形 ----
    x_arr = x_df.to_numpy()
    y_arr = y_df.to_numpy()

    print(x_arr[0:5])
    print(y_arr[0:5])

    # ---- (3) scikit-learnによる分析処理 ----
    # ラベルを数値に変換 ("setosa"->0, "versicolour"->1, "virginica"->2)
    le = LabelEncoder()
    y_arr = le.fit_transform(y_arr)
    print(y_arr[0:5])

    # モデルの学習
    model = learn_mlp(x_arr, y_arr)

    # スコアの確認
    # 0.9以上であればだいたいOK
    score = model.score(x_arr, y_arr)
    print("Score: %.2f" % (score))

    # モデルのテスト
    tmp_data = np.array([[7.4,2.8,6.1,1.9]])    # テストデータは行列(二次元配列)にする
    y = model.predict(tmp_data)

    for i in range(0, 4):
        print("%s: %f" % (df.columns[i], tmp_data[0][i]))
    print("の品種は")
    print(le.inverse_transform(y)[0])   # ラベルの数値を文字列に戻す

    return(0)


main()

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CODE: e-1 (seaborn, Pandas)

import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd


# ---- Config ----

CSV_FILE = "http://python.hisakatano.com/data/iris.csv"
OUTPUT_FILE = "e-1_output.svg"
matplotlib.rcParams['font.family'] = 'DejaVu Serif'


def main():
    df = pd.read_csv(CSV_FILE, index_col=None, header=0)
    print(df)

    bar_color = ["#479FFF", "#47DFFF", "#47FFC4"]

    sns.barplot(data=df, x="species", y="sepal_width", hue="species", palette=bar_color)
    plt.xlabel("")
    plt.title("Iris sepal width", fontsize=16)
    plt.savefig(OUTPUT_FILE)

    return(0)


main()

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CODE: e-2 (Graphviz, 関数)

import graphviz


DOT_FILE = "e-2_input.dot"
OUTPUT_FILE = "e-2_output"


def main():
    graph = graphviz.Source.from_file(DOT_FILE, format="png")
    print(graph)
    graph.render(filename=OUTPUT_FILE, cleanup=True)

    return(0)


main()

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