pandas 熊猫：根据值落在范围内的位置分配类别
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Pandas: assign category based on where value falls in range
提问by Johnny Metz
I have the following ranges and a pandas DataFrame:
我有以下范围和一个Pandas数据帧：
x >= 0 # success
-10 <= x < 0 # warning
X < -10 # danger
df = pd.DataFrame({'x': [2, 1], 'y': [-7, -5], 'z': [-30, -20]})
I'd like to categorize the values in the DataFrame based on where they fall within the defined ranges. So I'd like the final DF to look something like this:
我想根据它们在定义范围内的位置对 DataFrame 中的值进行分类。所以我希望最终的 DF 看起来像这样：
x y z x_cat y_cat z_cat
0 2 -7 -30 success warning danger
1 1 -5 -20 success warning danger
I've tried using the categorydatatype but it doesn't appear I can define a range anywhere.
我试过使用category数据类型，但似乎我不能在任何地方定义范围。
for category_column, value_column in zip(['x_cat', 'y_cat', 'z_cat'], ['x', 'y', 'z']):
df[category_column] = df[value_column].astype('category')
Can I use the categorydatatype? If not, what can I do here?
我可以使用category数据类型吗？如果没有，我可以在这里做什么？
回答by piRSquared
pandas.cut
pandas.cut
c = pd.cut(
df.stack(),
[-np.inf, -10, 0, np.inf],
labels=['danger', 'warning', 'success']
)
df.join(c.unstack().add_suffix('_cat'))
x y z x_cat y_cat z_cat
0 2 -7 -30 success warning danger
1 1 -5 -20 success warning danger
numpy
numpy
v = df.values
cats = np.array(['danger', 'warning', 'success'])
code = np.searchsorted([-10, 0], v.ravel()).reshape(v.shape)
cdf = pd.DataFrame(cats[code], df.index, df.columns)
df.join(cdf.add_suffix('_cat'))
x y z x_cat y_cat z_cat
0 2 -7 -30 success warning danger
1 1 -5 -20 success warning danger
回答by plasmon360
you could use assign to make new columns. for each new column use apply to filter the series.
您可以使用assign来创建新列。对于每个新列，使用 apply 来过滤系列。
df.assign(x_cat = lambda v: v.x.apply(lambda x: 'Sucess' if x>=0 else None),
y_cat = lambda v: v.y.apply(lambda x: 'warning' if -10<=x<0 else None),
z_cat = lambda v: v.z.apply(lambda x: 'danger' if x<=-10 else None),)
will result in
会导致
x y z x_cat y_cat z_cat
0 2 -7 -30 Sucess warning danger
1 1 -5 -20 Sucess warning danger
回答by FLab
You can use pandas cut, but you need to apply it column by column (just because the function operates on 1-d input):
您可以使用 pandas cut，但您需要逐列应用它（只是因为该函数对一维输入进行操作）：
labels = df.apply(lambda x: pd.cut(x, [-np.inf, -10, 0, np.inf], labels = ['danger', 'warning', 'success']))
x y z
0 success warning danger
1 success warning danger
So you can do:
所以你可以这样做：
pd.concat([df, labels.add_prefix('_cat')], axis = 1)
x y z cat_x cat_y cat_z
0 2 -7 -30 success warning danger
1 1 -5 -20 success warning danger
回答by Woody Pride
You could write a little function and then pass each series to the function using apply:
您可以编写一个小函数，然后使用 apply 将每个系列传递给该函数：
df = pd.DataFrame({'x': [2, 1], 'y': [-7, -5], 'z': [-30, -20]})
def cat(x):
if x <-10:
return "Danger"
if x < 0:
return "Warning"
return "Success"
for col in df.columns:
df[col] = df[col].apply(lambda x: cat(x))
回答by Quentin
Here's a ternary method for this type of thing.
这是这种类型的三元方法。
filter_method = lambda x: 'success' if x >= 0 else 'warning' if (x < 0 and x >= -10) else 'danger' if x < -10 else None
df[category_column] = df[value_column].apply(filter_method)
