如何清理熊猫DataFrame

Question

我当前的DataFrame如下：

    0   1    2   3      4    5
0   NAN Name NAN Amount NAN Percentage
1   NAN A    NAN 28223  NAN 8.70%
2   NAN B    NAN NAN    NAN NAN
3   NAN C    NAN NAN    NAN NAN
4   NAN D    NAN 21871  NAN 6.80%
5   NAN E    NAN NAN    NAN NAN
6   NAN F    NAN NAN    NAN NAN
7   NAN G    NAN 21380  NAN 6.64%
8   NAN H    NAN NAN    NAN NAN
9   NAN I    NAN NAN    NAN NAN
10  NAN J    NAN 20784  NAN 6.46%
11  NAN K    NAN NAN    NAN NAN
12  NAN L    NAN NAN    NAN NAN

我想通过将一些NAN值和一些NAN值删除到Not Listed来清理此DataFrame。

预期产量

Name Amount     Percentage
A    28223      8.70%
B    Not Listed Not Listed
C    Not Listed Not Listed
D    21871      6.80%
E    Not Listed Not Listed
F    Not Listed Not Listed
G    21380      6.64%
H    Not Listed Not Listed
I    Not Listed Not Listed
J    20784      6.46%
K    Not Listed Not Listed
L    Not Listed Not Listed

有人可以帮我吗

Answer 1

dropna + slicing

t = df.dropna(axis=1, how='all').values
pd.DataFrame(t[1:], columns=t[0]).fillna('Not listed')

   Name      Amount  Percentage
0     A       28223       8.70%
1     B  Not listed  Not listed
2     C  Not listed  Not listed
3     D       21871       6.80%
4     E  Not listed  Not listed
5     F  Not listed  Not listed
6     G       21380       6.64%
7     H  Not listed  Not listed
8     I  Not listed  Not listed
9     J       20784       6.46%
10    K  Not listed  Not listed
11    L  Not listed  Not listed

Answer 2

您可以使用df.drop([column_names],axis=1)删除不需要的列，并用NAN替换not listed，然后设置列标题并删除为标题花费的多余行。

df =df.drop([0,2,4],axis=1).replace(np.nan, 'Not Listed')
df.columns = df.iloc[0]
df.drop(0,inplace=True)

Answer 3

给出您特定的数据结构：

df.columns = df.iloc[0, :]  # Rename the columns based on the first row of data.
df.columns.name = None  # Set the columns name to None.
df = df.iloc[1:, :].reset_index(drop=True)  # Drop the column names from the data in the dataframe.
>>> df.replace('NAN', np.nan).dropna(how='all', axis=1).replace(np.nan, 'Not Listed')
   Name      Amount  Percentage
0     A       28223       8.70%
1     B  Not Listed  Not Listed
2     C  Not Listed  Not Listed
3     D       21871       6.80%
4     E  Not Listed  Not Listed
5     F  Not Listed  Not Listed
6     G       21380       6.64%
7     H  Not Listed  Not Listed
8     I  Not Listed  Not Listed
9     J       20784       6.46%
10    K  Not Listed  Not Listed
11    L  Not Listed  Not Listed

如果需要，可以通过更改代码的最后一行来将索引设置为名称：

>>> >>> df.replace('NAN', np.nan).dropna(how='all', axis=1).replace(np.nan, 'Not Listed').set_index('Name')
          Amount  Percentage
Name                        
A          28223       8.70%
B     Not Listed  Not Listed
C     Not Listed  Not Listed
D          21871       6.80%
E     Not Listed  Not Listed
F     Not Listed  Not Listed
G          21380       6.64%
H     Not Listed  Not Listed
I     Not Listed  Not Listed
J          20784       6.46%
K     Not Listed  Not Listed
L     Not Listed  Not Listed