In Pandas, you can replace NaN (Not-a-Number) values in a DataFrame with None (Python's None type) or np.nan (NumPy's NaN) values. Here's how you can replace NaN values with None: import pandas as pd import numpy as np # Create a sample DataFrame with NaN values data = {'A'...
'Bob',np.nan,'David'],'Age':[24,np.nan,22,23],'City':['New York','Los Angeles',np.nan,'Chicago']}df=pd.DataFrame(data)# 显示原始数据框print("原始数据框:")print(df)# 使用replace方法替换空值df.replace(np.nan,'未知',inplace=True)# 显示替换后的数据框print("\n替换空值后的数据...
在其他地方，我有另一个int-column，我想将其格式化为{:1f}，但它有时也包含NaN，因为我使用=IFERROR...
python # 创建一个包含NaN值的DataFrame df_with_nan = pd.DataFrame({ 'A': [1, 2, None, 4], 'B': [None, 2, 3, 4] }) # 使用replace方法替换NaN值(注意:在新版本中,更推荐使用fillna方法) df_replaced_nan = df_with_nan.replace(to_replace=pd.NA, value=0) # 或者使用 df_with_nan...
问使用df.replace可以用除np.nan/None以外的任何值替换pd.NaTEN读取数据 使用 pd 的 read_sql 读取...
用None代替0,我们可以像这样使用numpy.nan: >>> import numpy as np>>> temp["Glucose"] = diabetes_data["Glucose"].replace(0, np.nan)>>> temp.loc[null_index] Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome75 1 NaN 48 20 0 24.7 0.140 22 0182...
df['value'] = df['value'].replace(-np.inf, np.nan) df['value'] = df['value'].replace(np.inf, np.nan) or use pandas instead edwardluohaoadded thebugSomething isn't workinglabelJun 30, 2024 Contributor wence-commentedJul 1, 2024 ...
用None代替0,我们可以像这样使用numpy.nan: >>> import numpy as np>>> temp["Glucose"] = diabetes_data["Glucose"].replace(0, np.nan)>>> temp.loc[null_index] Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome75 1 NaN 48 20 0 24.7 0.140 22 0182...
The replace method is expected to replace all values in the df Series that match the keys in replace_dict with their corresponding values. However, the replacement stops once a NaN ("string", pd.options.future.infer_string = True) value is encountered, and the subsequent values are not rep...
Here, we’ll replace the value-1with the valueNaN. Let’s take a look: sales_data.replace(to_replace = -1, value = np.NaN) OUT: name region sales expenses 0 William East 50000.0 42000.0 1 Emma N 52000.0 43000.0 2 Sofia East 90000.0 -999.0 ...