Python 尝试将数据帧与分类数据连接时出现意外错误
我有两个数据帧df1和df2,看起来像这样:Python 尝试将数据帧与分类数据连接时出现意外错误,python,pandas,concatenation,categorical-data,Python,Pandas,Concatenation,Categorical Data,我有两个数据帧df1和df2,看起来像这样: #df1 counts freqs categories automatic 13 0.40625 manual 19 0.59375 #df2 counts freqs categories Straight Eng
#df1
counts freqs
categories
automatic 13 0.40625
manual 19 0.59375
#df2
counts freqs
categories
Straight Engine 18 0.5625
V engine 14 0.4375
谁能解释一下为什么pd.concat([df1,df2],axis=1)不会给我这个:
counts freqs
categories
automatic 13 0.40625
manual 19 0.59375
Straight Engine 18 0.5625
V engine 14 0.4375
以下是我尝试过的: 1-使用
pd.concat()
我怀疑我构建这些数据帧的方式可能是问题的根源。
下面是我如何得到这些特定数据帧的:
# imports
import pandas as pd
from pydataset import data # pip install pydataset to get datasets from R
# load data
df_mtcars = data('mtcars')
# change dummyvariables to more describing variables:
df_mtcars['am'][df_mtcars['am'] == 0] = 'manual'
df_mtcars['am'][df_mtcars['am'] == 1] = 'automatic'
df_mtcars['vs'][df_mtcars['vs'] == 0] = 'Straight Engine'
df_mtcars['vs'][df_mtcars['vs'] == 1] = 'V engine'
# describe categorical variables
df1 = pd.Categorical(df_mtcars['am']).describe()
df2 = pd.Categorical(df_mtcars['vs']).describe()
我理解“类别”是造成问题的原因,因为df_con=pd。concat([df1,df2],axis=1)
引发了以下错误:
TypeError:追加时类别必须与现有类别匹配
但我很困惑,这没关系:
# code
df_con = pd.concat([df1, df2], axis = 1)
# output:
counts freqs counts freqs
categories
automatic 13.0 0.40625 NaN NaN
manual 19.0 0.59375 NaN NaN
Straight Engine NaN NaN 18.0 0.5625
V engine NaN NaN 14.0 0.4375
2-使用df.append()
3-使用pd.merge()
某种程度上是可行的,但我正在丢失索引:
# Code
df_merge = pd.merge(df1, df2, how = 'outer')
# Output
counts freqs
0 13 0.40625
1 19 0.59375
2 18 0.56250
3 14 0.43750
3-在转置数据帧上使用pd.concat()
由于pd.concat()
# df1.T
categories automatic manual
counts 13.00000 19.00000
freqs 0.40625 0.59375
# df2.T
categories Straight Engine V engine
counts 18.0000 14.0000
freqs 0.5625 0.4375
但仍然没有成功:
# code
df_con = pd.concat([df1.T, df2.T], axis = 1)
>>> TypeError: categories must match existing categories when appending
顺便说一下,我在这里希望的是:
categories automatic manual Straight Engine V engine
counts 13.00000 19.00000 18.0000 14.0000
freqs 0.40625 0.59375 0.5625 0.4375
仍然适用于轴=0的情况,尽管:
# code
df_con = pd.concat([df1.T, df2.T], axis = 0)
# Output
categories automatic manual Straight Engine V engine
counts 13.00000 19.00000 NaN NaN
freqs 0.40625 0.59375 NaN NaN
counts NaN NaN 18.0000 14.0000
freqs NaN NaN 0.5625 0.4375
但这离我想要实现的目标还很远
现在我在想,从df1和df2中剥离“类别”信息是可能的,但我还没有找到如何做到这一点
谢谢你的任何其他建议 试试这个
pd.concat([df1.reset_index(),df2.reset_index()],ignore_index=True)
输出:
categories counts freqs
0 automatic 13 0.40625
1 manual 19 0.59375
2 Straight Engine 18 0.56250
3 V engine 14 0.43750
counts freqs
categories
automatic 13 0.40625
manual 19 0.59375
Straight Engine 18 0.56250
V engine 14 0.43750
要再次获取类别作为索引,请执行以下操作:
pd.concat([df1.reset_index(),df2.reset_index()],ignore_index=True).set_index('categories')
输出:
categories counts freqs
0 automatic 13 0.40625
1 manual 19 0.59375
2 Straight Engine 18 0.56250
3 V engine 14 0.43750
counts freqs
categories
automatic 13 0.40625
manual 19 0.59375
Straight Engine 18 0.56250
V engine 14 0.43750
有关更多详细信息,请参见,这是因为您拥有类别索引,如果您重置索引并执行concat,它将正常工作。对于concat操作,它基于索引。