Pandas 填充 NaN 值 |
您所在的位置：网站首页 › 怎样填入空值 › Pandas 填充 NaN 值 |
|
DataFrame.fillna() 方法
使用 DataFrame.fillna() 方法用指定的值填充整个 DataFrame
用指定的值填充指定列的 NaN 值
本教程解释了我们如何使用 DataFrame.fillna() 方法用指定的值填充 NaN 值。 我们将在本文中使用下面的 DataFrame。 import numpy as np import pandas as pd roll_no = [501, 502, 503, 504, 505] student_df = pd.DataFrame( { "Roll No": [501, 502, np.nan, 504, 505, 506], "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"], "Income(in $)": [200, 400, np.nan, 30, np.nan, np.nan], "Age": [17, 18, np.nan, 16, 18, np.nan], } ) print(student_df)输出： Roll No Name Income(in $) Age 0 501.0 Jennifer 200.0 17.0 1 502.0 Travis 400.0 18.0 2 NaN Bob NaN NaN 3 504.0 Emma 30.0 16.0 4 505.0 Luna NaN 18.0 5 506.0 Anish NaN NaN DataFrame.fillna() 方法 语法 DataFrame.fillna( value=None, method=None, axis=None, inplace=False, limit=None, downcast=None )DataFrame.fillna() 方法使我们能够用指定的值或方法来填充 DataFrame 中的 NaN 值。 使用 DataFrame.fillna() 方法用指定的值填充整个 DataFrame import numpy as np import pandas as pd roll_no = [501, 502, 503, 504, 505] student_df = pd.DataFrame( { "Roll No": [501, 502, np.nan, 504, 505, 506], "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"], "Income(in $)": [200, 400, np.nan, 30, np.nan, np.nan], "Age": [17, 18, np.nan, 16, 18, np.nan], } ) filled_df = student_df.fillna(0) print("DataFrame with NaN values") print(student_df, "\n") print("After applying fillna() to the DataFrame:") print(filled_df, "\n")输出： DataFrame with NaN values Roll No Name Income(in $) Age 0 501.0 Jennifer 200.0 17.0 1 502.0 Travis 400.0 18.0 2 NaN Bob NaN NaN 3 504.0 Emma 30.0 16.0 4 505.0 Luna NaN 18.0 5 506.0 Anish NaN NaN After applying fillna() to the DataFrame: Roll No Name Income(in $) Age 0 501.0 Jennifer 200.0 17.0 1 502.0 Travis 400.0 18.0 2 0.0 Bob 0.0 0.0 3 504.0 Emma 30.0 16.0 4 505.0 Luna 0.0 18.0 5 506.0 Anish 0.0 0.0它将 DataFrame student_df 中的所有 NaN 值替换为 0，该值作为参数传递给 DataFrame.fillna() 方法。 import numpy as np import pandas as pd roll_no = [501, 502, 503, 504, 505] student_df = pd.DataFrame( { "Roll No": [501, 502, np.nan, 504, 505, 506], "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"], "Income(in $)": [200, 400, np.nan, 30, np.nan, np.nan], "Age": [17, 18, np.nan, 16, 18, np.nan], } ) filled_df = student_df.fillna(method="ffill") print("DataFrame with NaN values") print(student_df, "\n") print("After applying fillna() to the DataFrame:") print(filled_df, "\n")输出： DataFrame with NaN values Roll No Name Income(in $) Age 0 501.0 Jennifer 200.0 17.0 1 502.0 Travis 400.0 18.0 2 NaN Bob NaN NaN 3 504.0 Emma 30.0 16.0 4 505.0 Luna NaN 18.0 5 506.0 Anish NaN NaN After applying fillna() to the DataFrame: Roll No Name Income(in $) Age 0 501.0 Jennifer 200.0 17.0 1 502.0 Travis 400.0 18.0 2 502.0 Bob 400.0 18.0 3 504.0 Emma 30.0 16.0 4 505.0 Luna 30.0 18.0 5 506.0 Anish 30.0 18.0它将所有 student_df 中的 NaN 值填入与 NaN 值相同列的 NaN 值之前的值。 用指定的值填充指定列的 NaN 值为了用指定的值来填充特定的值，我们向 fillna() 方法传递一个字典，以列名作为键，以该列的 NaN 值作为值。 import numpy as np import pandas as pd roll_no = [501, 502, 503, 504, 505] student_df = pd.DataFrame( { "Roll No": [501, 502, np.nan, 504, 505, 506], "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"], "Income(in $)": [200, 400, np.nan, 300, np.nan, np.nan], "Age": [17, 18, np.nan, 16, 18, np.nan], } ) filled_df = student_df.fillna({"Age": 17, "Income(in $)": 300}) print("DataFrame with NaN values") print(student_df, "\n") print("After applying fillna() to the DataFrame:") print(filled_df, "\n")输出： DataFrame with NaN values Roll No Name Income(in $) Age 0 501.0 Jennifer 200.0 17.0 1 502.0 Travis 400.0 18.0 2 NaN Bob NaN NaN 3 504.0 Emma 300.0 16.0 4 505.0 Luna NaN 18.0 5 506.0 Anish NaN NaN After applying fillna() to the DataFrame: Roll No Name Income(in $) Age 0 501.0 Jennifer 200.0 17.0 1 502.0 Travis 400.0 18.0 2 NaN Bob 300.0 17.0 3 504.0 Emma 300.0 16.0 4 505.0 Luna 300.0 18.0 5 506.0 Anish 300.0 17.0它将 Age 列中的所有 NaN 值填充为 17，将 Income(in $) 列中的所有 NaN 值填充为 300。Roll No 栏中的 NaN 值保持不变。 |
今日新闻 |
推荐新闻 |
| CopyRight 2018-2019 办公设备维修网 版权所有 豫ICP备15022753号-3 |