print('Colunm Name :', column) print('Column Contents :', columnSeriesObj.values) 输出: 方法4:以相反的顺序迭代列: 我们也可以以相反的顺序遍历列。 代码: import pandasaspd # List of Tuples students= [('Ankit',22,'A'), ('Swapnil',22,'B'), ('Priya',22,'B'), ('Shivangi',22,'...
read_excel('学生成绩汇总表.xlsx') print(df) 实例10:数据分箱:对数据进行分箱统计 import pandas as pd # 首先创建一个空的DataFrame df = pd.DataFrame(columns=['分箱']) # 然后建立一个列表数据,列表里面是人的姓名信息 box_list = [1, 4, 6, 7, 10, 13, 19, 20, 25, 30, 45, 48, ...
tt2 = ss1[ss1.notnull()] # 判断序列的非空值,效果同上 print(tt2) tt3 = ss1.dropna() # 清洗空值 print(tt3) # 序列切片 import pandas as pd import numpy as np s1 = pd.Series([1, -2, 2.3, 'hq']) s2 = pd.Series([1, -2, 2.3, 'hq'], index = ['a', 'b', 'c', '...
columns=['feature_one','feature_two','feature_three','feature_four'], index=['one','two','three']) # 输出 s_data print(s_data) # 访问第 1 列 print("访问第 1 列") print(s_data["feature_one"]) # 访问第 1 行 print("访问第 1 行") print(s_data.loc["one"]) # 访问第 ...
df.columns=columns_nameprint(df.head(3))#输出前3行print(df[['customername','reviews']]) 控制台输出: 4.按若干个列的组合条件筛选数据 importpandas as pd df=pd.read_csv('../hotel_csv_split/reviews_split_fenci_pos_1_05.csv',header=None,nrows=5)#在读数之后自定义标题columns_name=['mys...
print(df) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 现在我们遍历列为了遍历列,我们首先创建一个数据框列的列表,然后遍历列表。 # 创建数据框列的列表 columns=list(df) foriincolumns: # printing the third element of the column
# 每列的空值填充各自的均值forcolumnindf1.columns.tolist():m=df1[column].mean()# 列均值:mean可以改成max、min、mode等 df1[column]=df1[column].fillna(m)# 填充每个列 df1 .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; }...
() KeyError: 'a' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) Cell In[27], line 1 ---> 1 df.apply(f, axis="columns") File ~/work/pandas/pandas/pandas/core/frame.py:10374, in DataFrame.apply(self, func, axis, raw...
header=pd.MultiIndex.from_product([['Year1','Year2'],['Sales','Profit']])data_multiindex=[[100,10,120,15],[150,20,130,18]]df_wide=pd.DataFrame(data_multiindex,index=['StoreA','StoreB'],columns=header)print("原始宽格式 DataFrame (多级列索引):\n",df_wide)# 使用stack()将最内层...
DataFrame(data_dict) print("1.使用字典创建DataFrame: \n", df_dict) # 使用NumPy数组创建DataFrame data_numpy = np.array([[1, 'Alice'], [2, 'Bob'], [3, 'Charlie']]) df_numpy = pd.DataFrame(data_numpy, columns=['ID', 'Name']) print("2.使用NumPy数组创建DataFrame: \n", df_...