# Fill missing values in the dataset with a specific valuedf = df.fillna(0)# Replace missing values in the dataset with mediandf = df.fillna(df.median())# Replace missing values in Order Quantity column with the mean of Order Quantitiesdf['Order Quantity'].fillna(df["Order Quantity"].m...
# importing pandas packageimportpandasaspd# making data frame from csv filedata=pd.read_csv("employees.csv")# creating bool series True for NaN valuesbool_series=pd.notnull(data["Gender"])# filtering data# displaying data only with Gender = Not NaNdata[bool_series] 使用fillna()、replace()...
# creating bool series True for NaN values bool_series = pd.notnull(data["Gender"]) # filtering data # displayind data only with Gender = Not NaN data[bool_series] 产出: 如输出映像所示,只有具有Gender = NOT NULL都会显示。 使用fillna(), replace()和interpolate() 使用fillna(), replace()...
fillna(df.median()) # Replace missing values in Order Quantity column with the mean of Order Quantities df['Order Quantity'].fillna(df["Order Quantity"].mean, inplace=True) 检查重复行 duplicate()方法可以查看重复的行。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Check duplicate ...
# creating bool series True for NaN values bool_series = pd.notnull(data["Gender"]) # filtering data # displayind data only with Gender = Not NaN data[bool_series] 产出: 如输出映像所示,只有具有Gender = NOT NULL都会显示。 使用fillna(), replace()和interpolate() ...
interpolate(): Fill missing values using linear interpolation. These methods, along withfillna(), provide a comprehensive suite of tools for handling missing data in a variety of contexts. In conclusion, this article has demonstrated how to usedictto replace missing values in a Pandas DataFrame. ...
# importing pandas packageimportpandasaspdimportnumpyasnp# create a seriess=pd.Series([np.nan,np.nan,89,64,np.nan],index=["a","b","c","d","e"])print(s)# replace Missing values with 10result=s.fillna(10)print('Result:')print(result) ...
nan_result_df7=df.replace(np.nan,0) #用Pandas的replace替换缺失值 print(nan_result_df7) col1 col2 col3 col4 0 -0.977511 -0.566332 -0.529934 1.489695 1 -0.491128 0.000000 -0.811174 -1.102717 2 0.385777 -0.638822 0.325953 -0.240780 3 0.938351 -0.746889 0.375200 -0.715265 4 1.103418 0.238959 ...
# Replace missing values with a number df['ST_NUM'].fillna(125, inplace=True)# 125替换缺失值 或者可以用赋值的方式: # Location based replacement df.loc[2,'ST_NUM']=125 用该列的中值替换缺失值: # Replace using median median=df['NUM_BEDROOMS'].median() ...
printdf.isnull().values.any()# 检测是否有缺失值 Out: True# True表示有缺失值 计算所有缺失值的总数: # Total number of missing values printdf.isnull().sum().sum() Out: 8# 共有8个缺失值 7. 缺失值替换 用单一值替换缺失值: # Replace mi...