feat: bfs풀기

Author: chae-heechan

.idea/Programmers_Python_Algorithm_Study.iml

@@ -0,0 +1,29 @@
+from collections import Counter
+
+
+def solution():
+    answer = 0
+    n, k = map(int, input().split(" "))
+
+    s = list(map(int, input().split(" ")))
+    multitap = []
+
+    for i in range(k):
+        if len(multitap) < n and s[i] not in multitap:  # 멀티탭 자리가 있고 중복되지 않으면
+            multitap.append(s[i])
+        # 멀티탭 자리가 없고 중복되지 않은 경우
+        elif len(multitap) >= n and s[i] not in multitap:
+            answer += 1
+            min_tap = k + 1
+            for tap in multitap:
+                min(min_tap, s[i+1:].count(tap))
+        elif s[i] in multitap:    # 중복된 경우
+            continue
+
+    if len(multitap) >= n and s[k-1] not in multitap:
+        answer += 1
+
+    print(answer)
+
+
+solution()

BaekJoon/multitap.py

@@ -0,0 +1,38 @@
+def solution():
+    answer = 0
+    n, k = map(int, input().split(" "))
+
+    if k < 5:
+        print(0)
+        return
+
+    words = []
+
+    for i in range(n):
+        word = input()
+
+        word = word.replace("a", "")
+        word = word.replace("n", "")
+        word = word.replace("t", "")
+        word = word.replace("i", "")
+        word = word.replace("c", "")
+
+        print(word)
+        if len(word) == 0:
+            answer += 1
+            continue
+
+        if k - 5 >= len(word):
+            words.append(word)
+
+    words.sort(key=len)
+
+    print(words)
+
+    for word in words:
+
+
+
+
+
+solution()

BaekJoon/teaching.py

@@ -0,0 +1,5 @@
+import 
+
+public class bomboni {
+    BufferedReader br = new BufferedReader(new InputStreamReader(System.in));
+}

Java/bfs.java renamed to Java/BeakJoon/bfs.java

@@ -1,18 +1,95 @@
-import spacy
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
 
-nlp = spacy.load("en_core_web_sm")
+def main():
 
+    s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0,100,10))
+    s.plot()
+    plt.show()
 
-def anonymize_text(sentences):
-    answer = ""
-    words = nlp(sentences)
+    df = pd.DataFrame(np.random.randn(10,4).cumsum(0),
+        columns=['A','B','C','D'],
+        index=np.arange(0,100,10))
+    df.plot()
+    plt.show()
 
-    for word in words:
-        if word.tag_ == "NNP":
-            print("aaa")
-        answer += word.tag_ + " "
+    fig, axes = plt.subplots(2,1)
+    data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))
+    data.plot.bar(ax=axes[0], color='k', alpha=0.7)
+    data.plot.barh(ax=axes[1], color='k', alpha=0.7)
+    plt.show()
 
-    return answer
+    df = pd.DataFrame(np.random.rand(6,4), 
+        index=['one','two','three','four','five','six'],
+        columns=pd.Index(['A','B','C','D'], name='Genus'))
+    print(df)
+    df.plot.bar()
+    plt.show()
 
+    df.plot.barh(stacked=True, alpha=0.5)
+    plt.show()
 
-anonymize_text("John ate an apple Oh John")
+    tips = pd.read_csv('LEC/examples/tips.csv')
+    party_counts = pd.crosstab(tips['day'], tips['size'])
+    print(party_counts)
+    party_counts = party_counts.loc[:, 2:5]
+    party_pcts = party_counts.div(party_counts.sum(1), axis=0)
+    print(party_pcts)
+
+    party_pcts.plot.bar()
+    plt.show()
+
+    tips = pd.read_csv('LEC/examples/tips.csv')
+    tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])
+    print(tips.head())
+    sns.barplot(x='tip_pct', y='day', data=tips, orient='h')
+    plt.show()
+
+    sns.barplot(x='tip_pct', y='day', hue='time', data=tips, orient='h')
+    sns.set(style='whitegrid')
+    plt.show()
+
+    tips = pd.read_csv('LEC/examples/tips.csv')
+    tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])
+    tips['tip_pct'].plot.hist(bins=50) 
+    plt.show()
+
+    tips['tip_pct'].plot.density()
+    plt.show() 
+
+    tips['tip_pct'].plot.kde()
+    plt.show()
+
+    comp1 = np.random.normal(0,1,size=200)
+    comp2 = np.random.normal(10,2,size=200)
+
+    values = pd.Series(np.concatenate([comp1, comp2]))
+    sns.distplot(values, bins=100, color='k')
+    plt.show()
+
+    macro = pd.read_csv('LEC/examples/macrodata.csv')
+    data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]
+    trans_data = np.log(data).diff().dropna()
+    print(trans_data[-5:])
+
+    sns.regplot('m1', 'unemp', data=trans_data)
+    plt.title('Changes in log %s versus log %s' % ('m1', 'unemp'))
+    plt.show()
+
+    sns.pairplot(trans_data, diag_kind='kde', plot_kws={'alpha':0.2})
+    plt.show()
+
+    tips = pd.read_csv('LEC/examples/tips.csv')
+    tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])
+    sns.factorplot(x='day',y='tip_pct', hue='time', col='smoker', kind='bar', data=tips[tips.tip_pct < 1])
+    plt.show()
+
+    sns.factorplot(x='day', y='tip_pct', row='time', col='smoker', kind='bar', data=tips[tips.tip_pct < 1])
+    plt.show()
+    sns.factorplot(x='tip_pct', y='day', kind='box', data=tips[tips.tip_pct < 0.5])
+    plt.show()
+
+if __name__ == '__main__': # main()
+    main()