无法合并fi

Question

我试图合并两个文件，我提供他们的头，因为他们无法拿起头，当我合并他们使用连接，我得到一个错误，当我试图删除一列。。。。。。 ValueError:标签['左侧亚太地区.a2009s.meancurv']不包含在轴中 因此，我尝试下面的方法。。。。。你知道吗

标题很重要，因为我想在这些标题的基础上计算平均值，平均值等。。。。你知道吗

但是目前，结果文件看起来像this

CSV 1 CSV1 looks like thisCSV 2看起来与rh相同

# !/bin/bash
ls -d */ | sed -e "s/\///g" | grep -v "Results" | grep -v "Output">> subjects.txt;
module unload freesurfer
module load freesurfer/5.3.0
module load python
export SUBJECTS_DIR=/N/u/shrechak/Karst/GENFL_FREESURFER53_KARST_RES
source $FREESURFER_HOME/FreeSurferEnv.sh

aparcstats2table --hemi lh --subjectsfile=subjects.txt --parc aparc.a2009s --meas meancurv --tablefile lh.a2009s.meancurv.txt
aparcstats2table --hemi rh --subjectsfile=subjects.txt --parc aparc.a2009s --meas meancurv --tablefile rh.a2009s.meancurv.txt

for f in *.txt; do
    mv "$f" "${f%.txt}.csv"
done


python <<END_OF_PYTHON

import csv

import pandas as pd

names= ["meancurv",
"lh_G_and_S_frontomargin_meancurv",
"lh_G_and_S_occipital_inf_meancurv",
"lh_G_and_S_paracentral_meancurv",
"lh_G_and_S_subcentral_meancurv",
"lh_G_and_S_transv_frontopol_meancurv",
"lh_G_and_S_cingul-ant_meancurv",
"lh_G_and_S_cingul-Mid-Ant_meancurv",
"lh_G_and_S_cingul-Mid-Post_meancurv",
"lh_G_cingul-Post-dorsal_meancurv",
"lh_G_cingul-Post-ventral_meancurv",
"lh_G_cuneus_meancurv",
"lh_G_front_inf-Opercular_meancurv",
"lh_G_front_inf-orbital_meancurv",
"lh_G_front_inf-Triangul_meancurv",
"lh_G_front_middle_meancurv",
"lh_G_front_sup_meancurv",
"lh_G_Ins_lg_and_S_cent_ins_meancurv",
"lh_G_insular_short_meancurv",
"lh_G_occipital_middle_meancurv",
"lh_G_occipital_sup_meancurv",
"lh_G_oc-temp_lat-fusifor_meancurv",
"lh_G_oc-temp_med-Lingual_meancurv",
"lh_G_oc-temp_med-Parahip_meancurv",
"lh_G_orbital_meancurv",
"lh_G_pariet_infoangular_meancurv",
"lh_G_pariet_infSupramar_meancurv",
"lh_G_parietal_sup_meancurv",
"lh_G_postcentral_meancurv",
"lh_G_precentral_meancurv",
"lh_G_precuneus_meancurv",
"lh_G_rectus_meancurv",
"lh_G_subcallosal_meancurv",
"lh_G_temp_sup-G_T_transv_meancurv",
"lh_G_temp_sup-Lateral_meancurv",
"lh_G_temp_sup-Plan_polar_meancurv",
"lh_G_temp_supPlan_tempo_meancurv",
"lh_G_temporal_inf_meancurv",
"lh_G_temporal_middle_meancurv",
"lh_Lat_Fis-ant-Horizont_meancurv",
"lh_Lat_Fis-ant-Vertical_meancurv",
"lh_Lat_Fispost_meancurv",
"lh_Pole_occipital_meancurv",
"lh_Pole_temporal_meancurv",
"lh_S_calcarine_meancurv",
"lh_S_central_meancurv",
"lh_S_cingulMarginalis_meancurv",
"lh_S_circular_insula_ant_meancurv",
"lh_S_circular_insula_inf_meancurv",
"lh_S_circular_insula_sup_meancurv",
"lh_S_collat_transv_ant_meancurv",
"lh_S_collat_transv_post_meancurv",
"lh_S_front_inf_meancurv",
"lh_S_front_middle_meancurv",
"lh_S_front_sup_meancurv",
"lh_S_interm_prim-Jensen_meancurv",
"lh_S_intrapariet_and_P_trans_meancurv",
"lh_S_oc_middle_and_Lunatus_meancurv",
"lh_S_oc_sup_and_transversal_meancurv",
"lh_S_occipital_ant_meancurv",
"lh_S_oc-temp_lat_meancurv",
"lh_S_oc-temp_med_and_Lingual_meancurv",
"lh_S_orbital_lateral_meancurv",
"lh_S_orbital_med-olfact_meancurv",
"lh_S_orbital-H_Shaped_meancurv",
"lh_S_parieto_occipital_meancurv",
"lh_S_pericallosal_meancurv",
"lh_S_postcentral_meancurv",
"lh_S_precentral-inf-part_meancurv",
"lh_S_precentral-sup-part_meancurv",
"lh_S_suborbital_meancurv",
"lh_S_subparietal_meancurv",
"lh_S_temporal_inf_meancurv",
"lh_S_temporal_sup_meancurv",
"lh_S_temporal_transverse_meancurv"]

df1 = pd.read_csv('lh.a2009s.meancurv.csv', header = None, names = names)

names1 = ["meancurv",
"rh_G_and_S_frontomargin_meancurv",
"rh_G_and_S_occipital_inf_meancurv",
"rh_G_and_S_paracentral_meancurv",
"rh_G_and_S_subcentral_meancurv",
"rh_G_and_S_transv_frontopol_meancurv",
"rh_G_and_S_cingul-Ant_meancurv",
"rh_G_and_S_cingul-Mid-Ant_meancurv",
"rh_G_and_S_cingul-Mid-Post_meancurv",
"rh_G_cingul-Post-dorsal_meancurv",
"rh_G_cingul-Post-ventral_meancurv",
"rh_G_cuneus_meancurv",
"rh_G_front_inf-Opercular_meancurv",
"rh_G_front_inf-Orbital_meancurv",
"rh_G_front_inf-Triangul_meancurv",
"rh_G_front_middle_meancurv",
"rh_G_front_sup_meancurv",
"rh_G_Ins_lg_and_S_cent_ins_meancurv",
"rh_G_insular_short_meancurv",
"rh_G_occipital_middle_meancurv",
"rh_G_occipital_sup_meancurv",
"rh_G_oc-temp_lat-fusifor_meancurv",
"rh_G_oc-temp_med-Lingual_meancurv",
"rh_G_oc-temp_med-Parahip_meancurv",
"rh_G_orbital_meancurv",
"rh_G_pariet_inf-Angular_meancurv",
"rh_G_pariet_inf-Supramar_meancurv",
"rh_G_parietal_sup_meancurv",
"rh_G_postcentral_meancurv",
"rh_G_precentral_meancurv",
"rh_G_precuneus_meancurv",
"rh_G_rectus_meancurv",
"rh_G_subcallosal_meancurv",
"rh_G_temp_sup-G_T_transv_meancurv",
"rh_G_temp_sup-Lateral_meancurv",
"rh_G_temp_sup-Plan_polar_meancurv",
"rh_G_temp_sup-Plan_tempo_meancurv",
"rh_G_temporal_inf_meancurv",
"rh_G_temporal_middle_meancurv",
"rh_Lat_Fis-ant-Horizont_meancurv",
"rh_Lat_Fis-ant-Vertical_meancurv",
"rh_Lat_Fis-post_meancurv",
"rh_Pole_occipital_meancurv",
"rh_Pole_temporal_meancurv",
"rh_S_calcarine_meancurv",
"rh_S_central_meancurv",
"rh_S_cingulMarginalis_meancurv",
"rh_S_circular_insula_ant_meancurv",
"rh_S_circular_insula_inf_meancurv",
"rh_S_circular_insula_sup_meancurv",
"rh_S_collat_transv_ant_meancurv",
"rh_S_collat_transv_post_meancurv",
"rh_S_front_inf_meancurv",
"rh_S_front_middle_meancurv",
"rh_S_front_sup_meancurv",
"rh_S_interm_prim-Jensen_meancurv",
"rh_S_intrapariet_and_P_trans_meancurv",
"rh_S_oc_middle_and_Lunatus_meancurv",
"rh_S_oc_sup_and_transversal_meancurv",
"rh_S_occipital_ant_meancurv",
"rh_S_oc-temp_lat_meancurv",
"rh_S_oc-temp_med_and_Lingual_meancurv",
"rh_S_orbital_lateral_meancurv",
"rh_S_orbital_med-olfact_meancurv",
"rh_S_orbital-H_Shaped_meancurv",
"rh_S_parieto_occipital_meancurv",
"rh_S_pericallosal_meancurv",
"rh_S_postcentral_meancurv",
"rh_S_precentral-inf-part_meancurv",
"rh_S_precentral-sup-part_meancurv",
"rh_S_suborbital_meancurv",
"rh_S_subparietal_meancurv",
"rh_S_temporal_inf_meancurv",
"rh_S_temporal_sup_meancurv",
"rh_S_temporal_transverse_meancurv"
]

df2 = pd.read_csv('rh.a2009s.meancurv.csv', header = None, names = names1)

result = pd.merge(df1, df2, on='meancurv', how='outer')

result.to_csv('result.csv')





END_OF_PYTHON

echo "goodbye!";

Answer 1

这里有一种方法可以将两个文件合并到一起，并在合并后保留其中一个文件的标题。你知道吗

---

假设您在“文件”列表中保存文件：

files = ['file1.csv', 'file2.csv'] #keep files here
finalDF = pd.DataFrame() #this is an empty dataframe
for file in files:
    thisDF = pd.read_csv(file)
    finalDF = finalDF.append(thisDF, ignore_index=True)

现在如果你想试试这两行：

假设你想用一个简单的打印头来检查页眉（）

print finalDF.head()

如果你想把这个合并的数据帧写入csv文件

finalDF.to_csv('merged-file.csv', encoding="utf-8", index=False)

• 对于跳过行，您是要在之后还是之前跳过行 合并？告诉我，我也可以试着帮你。你知道吗

---

示例：

文件1.csv:

,column1,column2,column3,column4,Date,Device,sample_site
2,14888,0.060011931,248084,13.40535464,3/15/2017,DESKTOP,http://www.example1.com
11,1358,0.033212679,40888,7.465099785,3/15/2017,MOBILE,http://www.example2.com
23,130,0.02998155,4336,8.337638376,3/15/2017,TABLET,http://www.example3.com

文件2.csv:

,column1,column2,column3,column4,Date,Device,sample_site
35,2685,0.034564882,77680,10.97812822,3/15/2017,DESKTOP,https://www.example4.com
45,280,0.026197605,10688,7.801272455,3/15/2017,MOBILE,https://www.example5.com
54,24,0.022878932,1049,8.202097235,3/15/2017,TABLET,https://www.example6.com

合并-文件.csv：

Unnamed: 0,column1,column2,column3,column4,Date,Device,sample_site
2,14888,0.060011931,248084,13.40535464,3/15/2017,DESKTOP,http://www.example1.com
11,1358,0.033212679,40888,7.465099785,3/15/2017,MOBILE,http://www.example2.com
23,130,0.02998155,4336,8.337638376,3/15/2017,TABLET,http://www.example3.com
35,2685,0.034564882,77680,10.97812822,3/15/2017,DESKTOP,https://www.example4.com
45,280,0.026197605,10688,7.801272455,3/15/2017,MOBILE,https://www.example5.com
54,24,0.022878932,1049,8.202097235,3/15/2017,TABLET,https://www.example6.com

答复：

是否尝试基于列合并数据？在这种情况下，可以基于轴合并或合并join。你知道吗

比如说： pd.concat([df1, df2]) #add axis and join type if necessary。你知道吗

以下是帮助您理解的文档：merging and concat in pandas

Answer 2

所以你想跳过第一行，只拉数据部分。你知道吗

这是一个MCVE。你知道吗

代码：

import io
import pandas as pd

csv1 = io.StringIO(u'''
a,b,c
1,4,7
2,5,8
3,6,9
''')

df = pd.read_csv(csv1, names = ['d','e','f'], skiprows = [1])

print df

输出：

   d  e  f
0  1  4  7
1  2  5  8
2  3  6  9