Question

我的Googlefu让我失望了！

我有以下形式的大熊猫// The command with pipes. $command = 'command1 | command2 | echo Testing things | sed s/things/stuff/'; // Execute the command. The overall exit code is in $exitStatus. exec( $command . '; echo ${PIPESTATUS[*]}', $out, $exitStatus ); // Get the exit statuses and remove them from the output. $pipeStatus = explode(' ', array_pop($out)); print_r([$pipeStatus, $out]); // [ // [ // "127", // "127", // "0", // "0", // ], // [ // "Testing stuff", // ], // ]：

DataFrame

它基本上包含图表的节点，其中级别描述从较低级别到较高级别级别的传出边缘。我想转换DataFrame /创建一个新形式的DataFrame：

Level 1   Level 2   Level 3   Level 4
-------------------------------------
A         B         C         NaN
A         B         D         E
A         B         D         F
G         H         NaN       NaN
G         I         J         K

包含A B C D E F G H I J K --------------------------------------------- A | 0 1 0 0 0 0 0 0 0 0 0 B | 0 0 1 1 0 0 0 0 0 0 0 C | 0 0 0 0 0 0 0 0 0 0 0 D | 0 0 0 0 1 1 0 0 0 0 0 E | 0 0 0 0 0 0 0 0 0 0 0 F | 0 0 0 0 0 0 0 0 0 0 0 G | 0 0 0 0 0 0 0 1 1 0 0 H | 0 0 0 0 0 0 0 0 0 0 0 I | 0 0 0 0 0 0 0 0 0 1 0 J | 0 0 0 0 0 0 0 0 0 0 1 K | 0 0 0 0 0 0 0 0 0 0 0的单元格描绘了从相应行到相应列的传出边缘。在没有Pandas中的循环和条件的情况下，是否有Pythonic方法来实现这一目标？

Answer 1

试试这段代码：

df = pd.DataFrame({'level_1':['A', 'A', 'A', 'G', 'G'], 'level_2':['B', 'B', 'B', 'H', 'I'],
    'level_3':['C', 'D', 'D', np.nan, 'J'], 'level_4':[np.nan, 'E', 'F', np.nan, 'K']})

您的输入数据框是：

  level_1 level_2 level_3 level_4
0       A       B       C     NaN
1       A       B       D       E
2       A       B       D       F
3       G       H     NaN     NaN
4       G       I       J       K

解决方案是：

# Get unique values from input dataframe and filter out 'nan' values
list_nodes = []
for i_col in df.columns.tolist():
    list_nodes.extend(filter(lambda v: v==v, df[i_col].unique().tolist()))

# Initialize your result dataframe
df_res = pd.DataFrame(columns=sorted(list_nodes), index=sorted(list_nodes))
df_res = df_res.fillna(0)

# Get 'index-column' pairs from input dataframe ('nan's are exluded)
list_indexes = []
for i_col in range(df.shape[1]-1):
    list_indexes.extend(list(set([tuple(i) for i in df.iloc[:, i_col:i_col+2]\
        .dropna(axis=0).values.tolist()])))

# Use 'index-column' pairs to fill the result dataframe
for i_list_indexes in list_indexes:
    df_res.set_value(i_list_indexes[0], i_list_indexes[1], 1)

最终结果是：

   A  B  C  D  E  F  G  H  I  J  K
A  0  1  0  0  0  0  0  0  0  0  0
B  0  0  1  1  0  0  0  0  0  0  0
C  0  0  0  0  0  0  0  0  0  0  0
D  0  0  0  0  1  1  0  0  0  0  0
E  0  0  0  0  0  0  0  0  0  0  0
F  0  0  0  0  0  0  0  0  0  0  0
G  0  0  0  0  0  0  0  1  1  0  0
H  0  0  0  0  0  0  0  0  0  0  0
I  0  0  0  0  0  0  0  0  0  1  0
J  0  0  0  0  0  0  0  0  0  0  1
K  0  0  0  0  0  0  0  0  0  0  0

Pandas DataFrame具有图形节点级别和边到矩阵

1 个答案: