下面是将第一行作为列名的完整代码示例: importpandasaspd# 创建一个示例的DataFramedata={'A':[1,2,3],'B':[4,5,6],'C':[7,8,9]}df=pd.DataFrame(data)print("原始DataFrame:")print(df)# 将第一行作为列名df.columns=df.iloc[0]df=df[1:]print("\n将第一行作为列名后的DataFrame:")print...
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import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') # 插入列 df.insert(loc=2, column='爱好', value=None) # 保存修改后的DataFrame到新的Excel文件 df.to_excel('结果.xlsx', index=False) test() 3、插入多列 假设我需要在D列(班级)后面插入5列,表头名...
# 方法一:df[['education','salary']]# 方法二:df.iloc[:,1:] df[['education', 'salary']]和df['education', 'salary']不一样 (4) 提取第一列位置在1,10,15上的值 # 方法一:df.iloc[[1,10,15],0]# 方法二:df['createTime'][[1,10,15]]# 方法三:df['createTime'].take([1,10,...
复制 0 0 1 1 2 1 3 1 4 0 Name: Survived, dtype: int64 In [11]: 代码语言:javascript 代码运行次数:0 运行 复制 X = df_coded X.head() Out[11]: PassengerId Age Fare Pclass_2.0 Pclass_3.0 Pclass_nan Sex_male Sex_nan SibSp_1.0 SibSp_2.0 ... Parch_1.0 Parch_2.0 Parch_3.0 ...
df.column=col_name 指定列名 pandas数据筛选函数 函数名功能 df.columns 列名 df.index 索引名 df.shape 行x列 df.head(n=N) 前几行 df.tail(n=N) 后几行 df.values np对象的二维数组 df.reindex(index=[‘row1’…],columns=[‘col1’…] 重新排序 df[n:m] 切片,n~m-1 pandas数学运算和描述...
Unit Root Test Thenullhypothesisofthe Augmented Dickey-Fuller is that there is a unit root,withthe alternative that there is no unit root.That is to say the bigger the p-value the more reason we assert that there is a unit root''' def testStationarity(ts): dftest = adfuller(ts) # ...
Name: 0, dtype: object Note: We could also use thelocindexer to update one or multiple cells by row/column label. The code below sets the value130the first three cells or thesalarycolumn. survey_df.loc[[0,1,2],'salary'] = 130 ...
1. 向量化操作：避免循环，用 df.apply() 或内置函数 2. 使用合适的数据类型：如用 `category` 类型处理低基数文本 3. 分块处理大数据：`chunksize` 参数分批读取文件 1. Vectorization: Replace loops with `df.apply()` or built-in methods 2. Optimal Data Types: e.g., `category` for low-...
I came across this problem twice recently. For one case, I simply change the input dataframe into array and it works. For the second one, I have to realign the column names of the test dataframe using test_df = test_df[train_df.columns]. In both cases, the train_df and test_df ha...