确定元素在Datafram中的位置

Question

我正在搜索一个函数，它返回数据帧中元素的位置。 -数据帧中的值之间存在重复项 -数据帧约10*2000 -该函数将使用applymap（）应用于数据帧

# initial dataframe

    df = pandas.DataFrame({"R1": [8,2,3], "R2": [2,3,4], "R3": [-3,4,-1]})

Example:

get_position(2) is not clear as it could be either "R1" or "R2". I am wondering if there is another way that python knows which Position the element holds - possibly during the applymap() Operation

编辑：

你知道吗测向等级（轴=1，pct=真）

编辑2:

#intial dataframe

df_initial = pandas.DataFrame({"R1": [8,2,3], "R2": [2,3,4], "R3": [-3,4,-1]})

步骤1）

df_rank = df_initial.rank(axis=1,pct=True)

步骤2）

# Building Groups based on the percentage of the respective value

    def function103(x):

        if 0.0 <= x <= 0.1:
            P1.append(get_column_name1(x))
            return x
        elif 0.1 < x <= 0.2:
            P2.append(get_column_name1(x))
            return x
        elif 0.2 < x <= 0.3:
            P3.append(get_column_name1(x))
            return x
        elif 0.3 < x <= 0.4:
            P4.append(get_column_name1(x))
            return x
        elif 0.4 < x <= 0.5:
            P5.append(get_column_name1(x))
            return x
        elif 0.5 < x <= 0.6:
            P6.append(get_column_name1(x))
            return x
        elif 0.6 < x <= 0.7:
            P7.append(get_column_name1(x))
            return x
        elif 0.7 < x <= 0.8:
            P8.append(get_column_name1(x))
            return x
        elif 0.8 < x <= 0.9:
            P9.append(get_column_name1(x))
            return x
        elif 0.9 < x <= 1.0:
            P10.append(get_column_name1(x))
            return x
        else:
            return x

步骤3）

# trying to get the columns Name of the the respective value
# my idea was to determine the Position of each value to then write a function

    def get_column_name1(x)

#to return the values column Name

步骤4）

# apply the function

P1=[]
P2=[]
P3=[]
P4=[]
P5=[]
P6=[]
P7=[]
P8=[]
P9=[]
P10=[]
P11=[]
df_rank.applymap(function103).head()

Answer 1

如果需要数据帧中按值列出的索引或列名称，请使用^{}作为位置，然后选择转换为numpy数组的所有索引或列值：

df = pd.DataFrame({"R1": [8,2,3], "R2": [2,3,4], "R3": [-3,4,-1]})

i, c = np.where(df == 2)
print (i, c)
[0 1] [1 0]

print (df.index.values[i])
[0 1]

print (df.columns.values[c])
['R2' 'R1']

编辑：

i, c = np.where(df == 2)

df1 = df.rank(axis=1,pct=True)
print (df1)
         R1        R2        R3
0  1.000000  0.666667  0.333333
1  0.333333  0.666667  1.000000
2  0.666667  1.000000  0.333333

print (df1.iloc[i, c])
         R2        R1
0  0.666667  1.000000
1  0.666667  0.333333

print (df1.where(df == 2).dropna(how='all').dropna(how='all', axis=1))
         R1        R2
0       NaN  0.666667
1  0.333333       NaN

或：

out = df1.stack()[df.stack() == 2].rename_axis(('idx','cols')).reset_index(name='val')
print (out)
   idx cols       val
0    0   R2  0.666667
1    1   R1  0.333333

编辑：

函数的解决方案-需要通过重塑和提取创建的一列数据帧进行迭代系列名称，与列名相同：

def get_column_name1(x):
    return x.name

---

P1=[]
P2=[]
P3=[]
P4=[]
P5=[]
P6=[]
P7=[]
P8=[]
P9=[]
P10=[]
P11=[]

def function103(x):

    if 0.0 <= x[0] <= 0.1:
        P1.append(get_column_name1(x))
        return x
    elif 0.1 < x[0] <= 0.2:
        P2.append(get_column_name1(x))
        return x
    elif 0.2 < x[0] <= 0.3:
        P3.append(get_column_name1(x))
        return x
    elif 0.3 < x[0] <= 0.4:
        P4.append(get_column_name1(x))
        return x
    elif 0.4 < x[0] <= 0.5:
        P5.append(get_column_name1(x))
        return x
    elif 0.5 < x[0] <= 0.6:
        P6.append(get_column_name1(x))
        return x
    elif 0.6 < x[0] <= 0.7:
        P7.append(get_column_name1(x))
        return x
    elif 0.7 < x[0] <= 0.8:
        P8.append(get_column_name1(x))
        return x
    elif 0.8 < x[0] <= 0.9:
        P9.append(get_column_name1(x))
        return x
    elif 0.9 < x[0] <= 1.0:
        P10.append(get_column_name1(x))
        return x
    else:
        return x

---

a = df_rank.stack().reset_index(level=0, drop=True).to_frame().apply(function103, axis=1)

---

print (P4)
['R3', 'R1', 'R3']