>>> df.dropna(thresh=3, axis=1) A C D 0 1.0 1 NaN 1 1.0 1 1.0 2 1.0 1 1.0 3 NaN 1 1.0 如果你想用NaN的数量来推理: # example for a minimum of 2 NaN to drop the column >>> df.dropna(thresh=len(df.columns)-(2-1), axis=1)...
这是因为drop方法中,默认是删除行。 如果用axis=0或axis='rows',都表示展出行,也可用labels参数删除行。 df.drop(0) # drop a row, on axis 0 or 'rows' df.drop(0, axis=0) # same df.drop(0, axis='rows') # same df.drop(labels=0) # same df.drop(labels=[0]) # same # 结果 a ...
接下来是处理剩余行中的空值,经过测试,在 DataFrame.replace() 中使用空字符串,要比默认的空值NaN节省一些空间;但对整个CSV文件来说,空列只是多存了一个“,”,所以移除的9800万 x 6列也只省下了200M的空间。进一步的数据清洗还是在移除无用数据和合并上。 对数据列的丢弃,除无效值和需求规定之外,一些表自身...
4. 预测模型填补针对数值型数据，可以训练机器学习模型（如线性回归、决策树等）预测缺失值。Python1from sklearn.linear_model import LinearRegression2from sklearn.model_selection import train_test_split34# 示例：使用线性回归预测缺失值（需选择不含缺失值的列作为特征）5X = df.drop(['target_column_with_m...
# 第一种方法下删除column一定要指定axis=1,否则会报错 >>> df.drop(['B', 'C']) ValueError: labels ['B' 'C'] not contained in axis #Drop rows >>>df.drop([0, 1]) A B C D 2 8 9 10 11 >>> df.drop(index=[0, 1]) ...
].mode()[0], inplace=True) # 分类变量# 转换虚拟变量df_dummies = pd.get_dummies(df, drop...
Example 1: Remove Column from pandas DataFrame by Name This section demonstrates how to delete one particular DataFrame column by its name. For this, we can use the drop() function and the axis argument as shown below: data_new1=data.drop("x1",axis=1)# Apply drop() functionprint(data_...
William Henry male 35.0 0 0 373450 8.0500 NaN S In [3]: 代码语言:javascript 代码运行次数:0 运行 复制 df_train.drop(columns=["Name", "Ticket", "Cabin"], inplace=True) df_train.head() Out[3]: PassengerId Survived Pclass Sex Age SibSp Parch Fare Embarked 0 1 0 3 male 22.0 1 ...
1、单列drop,就是删除某一列 In [4]: 代码语言:javascript 代码运行次数:0 运行 复制 # 代表的就是删除某列 df.drop("A", axis=1) Out[4]: B C D 0 1 2 3 1 5 6 7 2 9 10 11 2、单行drop,就是删除某一行 In [5]: 代码语言:javascript 代码运行次数:0 运行 复制 df Out[5]: A ...
As shown in Table 2, the previous code has created a new pandas DataFrame, where all rows with one or multiple NaN values have been deleted. Example 2: Drop Rows of pandas DataFrame that Contain a Missing Value in a Specific Column ...