df1 = df.groupby('类名')['书名'].sum() df1 = df1.to_dict() for i, j in df1.items(): print(i, ':\t', j) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 结果展示: DataFrame转换为列表(tolist函数) 1).示例: import pandas as pd pd.set_option('display.unicode.ambiguou...
#random.sample(range(0,10),6)从0-9这十位数中随机选出6位 test_list=[]foriinrange(3000):test_list.append("123456"+"".join(str(s)forsinrandom.sample(range(0,10),6)))#生成3000个1-200的随机浮点数,且保留两位小数 test_list2=[round(random.uniform(1,200),2)for_inrange(3000)]data=...
# 使用 values 属性将 DataFrame 转换为 NumPy 数组,然后再转换为列表 list_from_values = df.values.tolist() print("列表 from values 属性:", list_from_values) 1. 2. 3. 4. 5. 6. 7. 8. 9. 2 使用to_numpy()方法 to_numpy()方法可以将 DataFrame 直接转换为 NumPy 数组,然后再将 NumPy ...
ENseaborn提供了一个快速展示数据库中列元素分布和相互关系的函数，即pairplot函数，该函数会自动选取数据...
dfpath=df[df['mm'].str.contains('20180122\d')].values dfplist=np.array(dfpath).tolist()
numpy as npdef list_comp(s): return [x.split() for x in s] # If you want an equality check #return pd.Series([x.split() for x in s], index=s.index)def series_apply(s): return s.apply(lambda x: x.split())def str_accessor(s): return s.str.split()perfplot.show( setup...
loc索引或切片(loc中可以取str): printdata.loc[0:1, ['one', 'three']] # 筛选出dataframe中有某一个或某几个字符串的列: list=['key1','key2'] df= df[df['one'].isin(list)] 筛选出dataframe中不含某一个或某几个字符串的列,相当于反选 ...
movie = ["战狼2","哪吒之魔童降世","流浪地球","红海行动"]piapofang = [str(x)+"亿" for x in [56.39,49.34,46.18,36.22]]list_to_tuple = list(zip(movie,piaofang))df = pd.DataFrame(list_to_tuple,columns=["movies","piaofang"])display(df) ...
df=pd.DataFrame(np.random.randn(6,4),columns=list('ABCD'))print(df)df['新增的列']=range(1,len(df)+1)df['新增的列2']=['abc','bc','cd','addc','dd','efsgs']print(df.head())print(len(df))#表示数据集有多少行,而不是列表中的字符串的长度print(df['新增的列2'].str.len(...
\n\n",df,"\n") list_of_single_column = df['DOB'].tolist() print("the list of a ...