# 假设 df 是你的 DataFrame missing_values = df.isnull().sum()print(missing_values)删除含有缺失值的行或列：- 删除行：当缺失值过多或对分析影响较大时，可以选择删除包含缺失值的行。df_cleaned = df.dropna()- 删除列：如果某一列的大部分数据都是缺失的，可以考虑删除该列
在数据处理过程中,有时会遇到DataFrame中缺少某些行的情况。为了保持数据的完整性和一致性,我们需要向DataFrame中添加这些缺失的行。以下是一些基础概念、相关优势、类型、应用场景以及解决方案...
df = pd.DataFrame(data) df_filled = df.fillna(method='bfill') print(df_filled) Themethod='bfill'parameter fills missing values using the next valid observation. This is useful for filling gaps in data. Filling with Column Mean This example demonstrates filling missing values with column means...
3. 多重插补多重插补是一种统计方法，通过生成一系列完整的数据集来处理缺失值，每个数据集中对缺失值的填补是随机生成的，反映了缺失值的不确定性。Python1from sklearn.impute import KNNImputer23# 使用K近邻法进行多重插补4imputer = KNNImputer(n_neighbors=5)5df_imputed = pd.DataFrame(imputer.fit_transfo...
imr=Imputer(missing_values='NaN',strategy='mean',axis=0)imputed_data=pd.DataFrame(imr.fit_transform(df.values),columns=df.columns)imputed_data 方式3:插值填充 采用某种插入模式进行填充,比如取缺失值前后值的均值进行填充: 代码语言:javascript
df = pd.DataFrame(dict) # using notnull() function df.notnull() 产出: 代码4: # importing pandas package import pandas as pd # making data frame from csv file data = pd.read_csv("employees.csv") # creating bool series True for NaN values ...
# Importing librariesimportpandasaspdimportnumpyasnp# Read csv file into a pandas dataframedf = pd.read_csv("property data.csv")# Take a look at the first few rowsprintdf.head() Out: ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS0104.0PUTNAM Y3.01197.0LEXINGTON N3.02NaN LEXINGTON N3.03201.0BERK...
LabelEncoder() # 将描述变量自动转化为 数值型变量 # 并将转化为的数据附加到原始数据上 for col in cat_vars: tran = le.fit_transform(df[col].tolist()) tran_df = pd.DataFrame(tran, columns=['num_'+col]) print("{col}经过转化为{num_col}".format(col=col,num_col='num_'+col)) # ...
2)Example 1: Drop Rows of pandas DataFrame that Contain One or More Missing Values 3)Example 2: Drop Rows of pandas DataFrame that Contain a Missing Value in a Specific Column 4)Example 3: Drop Rows of pandas DataFrame that Contain Missing Values in All Columns ...
Filling in for missing values np.random.seed(25) DF_obj = DataFrame(np.random.rand(36).reshape(6,6)) DF_obj DF_obj.loc[3:5,0] = missing DF_obj.loc[1:4,5] = missing DF_obj filled_DF = DF_obj.fillna(0) filled_DF filled_DF = DF_obj.fillna({0:0.1,5:1.25}) ...