代码: importpandasaspd# List of Tuplesstudents=[('Ankit',22,'A'),('Swapnil',22,'B'),('Priya',22,'B'),('Shivangi',22,'B'),]# Create a DataFrame objectstu_df=pd.DataFrame(students,columns=['Name','Age','Section'],
# nameusing[]operatorcolumnSeriesObj=stu_df[column] 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...
In [1]: arrays = [ ...: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ...: ["one", "two", "one", "two", "one", "two", "one", "two"], ...: ] ...: In [2]: tuples = list(zip(*arrays)) In [3]: tuples Out[3]: [('bar', 'one'...
In [12]: df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy() In [13]: df[['A', 'B']] Out[13]: A B 2000-01-01 0.469112 -0.282863 2000-01-02 1.212112 -0.173215 2000-01-03 -0.861849 -2.104569 2000-01-04 0.721555 -0.706771 2000-01-05 -0.424972 0.567020 2000-01-0...
'销售额'].sum().sort_values(ascending=False).reset_index() labels = df_sale['区域'].tolist...
import pandas as pd df_data = pd.read_csv(data_file, names=col_list) 显示原始数据,df_data.head() 运行apply函数,并记录该操作耗时: for col in df_data.columns: df_data[col] = df_data.apply(lambda x: apply_md5(x[col]), axis=1) 显示结果数据,df_data.head() 2. Polars测试 Polars...
# converting back to DataFrame df4 = pd.DataFrame(df_dict) end = time.time() print(end - start) ## Time taken: 31 seconds 字典方法大约需要31秒,大约比' itertuples() '函数快11倍。 数组列表 我们还可以将DataFrame转换为一个数组,遍历该数组以对每行(存储在...
print(s_from_list_custom_index) # 输出: # a 10 # b 20 # c 30 # d 40 # e 50 # dtype: int64 # 2. 从 NumPy 数组创建 Series # 这是非常常见的方式,因为 Pandas 底层大量依赖 NumPy np_array = np.array([1.1,2.2,3.3,4.4,5.5])# 定义一个NumPy数组 ...
3)使用 itertuples() 设置 index=False,去除索引作为元组的第一个元素 import pandas as pd # 创建一个 DataFrame df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]}, index=['dog', 'hawk']) # 使用 itertuples() 设置 index=False,去除索引作为元组的第一个元素 print("\n使...
into one list of (key,value) tuples: zippedzipped =list(zip(list_keys,list_values))# Inspect the list using print()print(zipped)# Build a dictionary with the zipped list: datadata =dict(zipped)# Build and inspect a DataFrame from the dictionary: dfdf = pd.DataFrame(data)print(df) ...