当一个或多个项目或整个单元没有提供信息时，可能会出现丢失数据。在现实生活中，丢失数据是一个很大的问题，往往找半天还找不回来。
在Pandas中，缺少的数据由两个值表示：
- None：None是Python单例对象，通常用于丢失Python代码中的数据。
- NaN(非数字的缩写)，是所有使用标准ieee浮点表示的系统所认可的特殊浮点值。
pandas对于None和NaN本质上是可互换的，用于表示缺失或空值。
在Pandas DataFrame中有几个用于检测、删除和替换空值的有用函数：
- isnull()
- notnull()
- dropna()
- fillna()
- replace()
- interpolate()
使用isnull()和notnull()
使用函数isnull()和notnull()检查PandasDataFrame中缺少的值。
使用isnull()
为了检查PandasDataFrame中的空值，我们使用isnull()函数返回布尔值的数据，这些值是NaN值的真值。
代码1：
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, 45, 56, np.nan],
'Third Score':[np.nan, 40, 80, 98]}
# creating a dataframe from list
df = pd.DataFrame(dict)
# using isnull() function
df.isnull()
产出：
代码2：
# 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
bool_series = pd.isnull(data["Gender"])
# filtering data
# displaying data only with Gender = NaN
data[bool_series]
产出：
如输出映像所示，只有具有Gender = NULL都会显示。
使用notnull()
为了检查PandasDataframe中的空值，我们使用NOTNULL()函数来返回对于NaN值为false的布尔值的数据。
代码3：
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, 45, 56, np.nan],
'Third Score':[np.nan, 40, 80, 98]}
# creating a dataframe using dictionary
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
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()和interpolate()函数这些函数将NaN值替换为它们自己的一些值。在DataFrame的数据集中填充空值。
插值()函数主要用于填充NA数据中的值，使用各种插值技术来填充丢失的值，不是对值进行硬编码。
**代码1：**用单个值填充空值
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, 45, 56, np.nan],
'Third Score':[np.nan, 40, 80, 98]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
# filling missing value using fillna()
df.fillna(0)
产出：
**代码2：**用前面的值填充空值
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, 45, 56, np.nan],
'Third Score':[np.nan, 40, 80, 98]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
# filling a missing value with
# previous ones
df.fillna(method ='pad')
产出：
**代码3：**用下一个值填充空值
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, 45, 56, np.nan],
'Third Score':[np.nan, 40, 80, 98]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
# filling null value using fillna() function
df.fillna(method ='bfill')
产出：
**代码4：**在CSV文件中填充空值
# importing pandas package
import pandas as pd
# making data frame from csv file
data = pd.read_csv("employees.csv")
# Printing the first 10 to 24 rows of
# the data frame for visualization
data[10:25]
现在，我们将用“无性别”填充性别列中的所有空值。
# importing pandas package
import pandas as pd
# making data frame from csv file
data = pd.read_csv("employees.csv")
# filling a null values using fillna()
data["Gender"].fillna("No Gender", inplace = True)
data
产出：
**代码5：**使用替换()方法填充空值
# importing pandas package
import pandas as pd
# making data frame from csv file
data = pd.read_csv("employees.csv")
# Printing the first 10 to 24 rows of
# the data frame for visualization
data[10:25]
产出：
现在，我们将将数据帧中的ALNAN值替换为-99值。
# importing pandas package
import pandas as pd
# making data frame from csv file
data = pd.read_csv("employees.csv")
# will replace Nan value in dataframe with value -99
data.replace(to_replace = np.nan, value = -99)
产出：
**代码6：**使用插值()函数来使用线性方法填充缺失的值。
# importing pandas as pd
import pandas as pd
# Creating the dataframe
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
"B":[None, 2, 54, 3, None],
"C":[20, 16, None, 3, 8],
"D":[14, 3, None, None, 6]})
# Print the dataframe
df
让我们用线性方法插值缺失的值。请注意，线性方法忽略索引，并将值视为等距。
# to interpolate the missing values
df.interpolate(method ='linear', limit_direction ='forward')
产出：
正如我们可以看到的输出，第一行中的值无法被填充，因为填充值的方向是向前的，并且没有以前的值可以用于插值。
使用dropna()
从dataframe中删除空值，使用dropna()函数以不同的方式删除具有Null值的数据集的行/列。
**代码1：**删除至少1空值的行。
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, np.nan, 45, 56],
'Third Score':[52, 40, 80, 98],
'Fourth Score':[np.nan, np.nan, np.nan, 65]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
df
使用至少一个Nan值(Null值)删除行。
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, np.nan, 45, 56],
'Third Score':[52, 40, 80, 98],
'Fourth Score':[np.nan, np.nan, np.nan, 65]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
# using dropna() function
df.dropna()
产出：
**代码2：**如果该行中的所有值都丢失，则删除行。
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, np.nan, np.nan, 95],
'Second Score': [30, np.nan, 45, 56],
'Third Score':[52, np.nan, 80, 98],
'Fourth Score':[np.nan, np.nan, np.nan, 65]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
df
删除所有数据丢失或包含空值(Nan)的行。
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, np.nan, np.nan, 95],
'Second Score': [30, np.nan, 45, 56],
'Third Score':[52, np.nan, 80, 98],
'Fourth Score':[np.nan, np.nan, np.nan, 65]}
df = pd.DataFrame(dict)
# using dropna() function
df.dropna(how = 'all')
产出：
**代码3：**删除至少1空值的列。
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, np.nan, np.nan, 95],
'Second Score': [30, np.nan, 45, 56],
'Third Score':[52, np.nan, 80, 98],
'Fourth Score':[60, 67, 68, 65]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
df
删除至少有1个缺失值的列。
# importing pandas as pd
import pandas as pd
# importing numpy as np
import numpy as np
# dictionary of lists
dict = {'First Score':[100, np.nan, np.nan, 95],
'Second Score': [30, np.nan, 45, 56],
'Third Score':[52, np.nan, 80, 98],
'Fourth Score':[60, 67, 68, 65]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
# using dropna() function
df.dropna(axis = 1)
产出：
**代码4：**在CSV文件中删除至少1空值的行
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("employees.csv")
# making new data frame with dropped NA values
new_data = data.dropna(axis = 0, how ='any')
new_data
产出：
现在我们比较数据帧的大小，这样我们就可以知道有多少行至少有一个空值。
print("Old data frame length:", len(data))
print("New data frame length:", len(new_data))
print("Number of rows with at least 1 NA value: ", (len(data)-len(new_data)))
产出：
Old data frame length: 1000
New data frame length: 764
Number of rows with at least 1 NA value: 236
由于差值为236，因此在任何列中都有236行，其中至少有1空值。
