Pandas 将头添加到文件中的.data文件
给定一个扩展名为Pandas 将头添加到文件中的.data文件,pandas,read-fwf,Pandas,Read Fwf,给定一个扩展名为.data的文件，我已经用pd.read\u fwf（“./input.data”，sep=“，”，header=None）阅读了它。： 输出： 如何向其中添加以下列名？谢谢 col_names = ["age", "sex", "cp", "restbp", "chol", "fbs", "restecg", &qu
.datapd.read\u fwf（“./input.data”，sep=“，”，header=None）阅读了它。col_names = ["age", "sex", "cp", "restbp", "chol", "fbs", "restecg",
"thalach", "exang", "oldpeak", "slope", "ca", "thal", "num"]
pd.read_fwf("./input.data", names = col_names)
，只需在不带标题的情况下执行
读取\u csv列名称df = pd.read_csv('input.data', header=None, names=col_names);
如何工作
pd.read\u csv（“/input.data”，name=col\u name）文件还是csv
？这对使用哪种方法产生了巨大的影响。read\u csv
解决了这个问题：）@谢谢您在名称
参数之外的更新。这正是我所需要的，目前还没有为read_fwf
函数编写文档。我很高兴它对您有所帮助，不客气。我已经尝试过这一点，但得到了许多带有NaN
s的专栏。我还添加了sep=”，“
，它不起作用。@ahbon-一个想法，如何为noquoting添加参数quoting=3
？我添加了quoting=3
，结果没有改变。@ahbon-数据是否保密？似乎出现了一些与数据相关的问题，我使用了read\u fwf
而不是read\u csv
。header=None是不必要的，因为如果通过names
则默认设置为header=None
age sex cp restbp chol fbs restecg thalach exang oldpeak slope ca thal num
0 63.0,1.0,1.0,145.0,233.0,1.0,2.0,150.0,0.0,2.3... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 67.0,1.0,4.0,160.0,286.0,0.0,2.0,108.0,1.0,1.5... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 67.0,1.0,4.0,120.0,229.0,0.0,2.0,129.0,1.0,2.6... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 37.0,1.0,3.0,130.0,250.0,0.0,0.0,187.0,0.0,3.5... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 41.0,0.0,2.0,130.0,204.0,0.0,2.0,172.0,0.0,1.4... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
292 57.0,0.0,4.0,140.0,241.0,0.0,0.0,123.0,1.0,0.2... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
293 45.0,1.0,1.0,110.0,264.0,0.0,0.0,132.0,0.0,1.2... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
294 68.0,1.0,4.0,144.0,193.0,1.0,0.0,141.0,0.0,3.4... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
295 57.0,1.0,4.0,130.0,131.0,0.0,0.0,115.0,1.0,1.2... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
296 57.0,0.0,2.0,130.0,236.0,0.0,2.0,174.0,0.0,0.0... NaN NaN NaN NaN NaN NaN
col_names = ["age", "sex", "cp", "restbp", "chol", "fbs", "restecg",
"thalach", "exang", "oldpeak", "slope", "ca", "thal", "num"]
df = pd.read_csv("input.data", names=col_names)
print (df)
age sex cp restbp chol fbs restecg thalach exang oldpeak \
0 63.0 1.0 1.0 145.0 233.0 1.0 2.0 150.0 0.0 2.3
1 67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5
2 67.0 1.0 4.0 120.0 229.0 0.0 2.0 129.0 1.0 2.6
3 37.0 1.0 3.0 130.0 250.0 0.0 0.0 187.0 0.0 3.5
4 41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4
.. ... ... ... ... ... ... ... ... ... ...
292 57.0 0.0 4.0 140.0 241.0 0.0 0.0 123.0 1.0 0.2
293 45.0 1.0 1.0 110.0 264.0 0.0 0.0 132.0 0.0 1.2
294 68.0 1.0 4.0 144.0 193.0 1.0 0.0 141.0 0.0 3.4
295 57.0 1.0 4.0 130.0 131.0 0.0 0.0 115.0 1.0 1.2
296 57.0 0.0 2.0 130.0 236.0 0.0 2.0 174.0 0.0 0.0
slope ca thal num
0 3.0 0.0 6.0 0
1 2.0 3.0 3.0 1
2 2.0 2.0 7.0 1
3 3.0 0.0 3.0 0
4 1.0 0.0 3.0 0
.. ... ... ... ...
292 2.0 0.0 7.0 1
293 2.0 0.0 7.0 1
294 2.0 2.0 7.0 1
295 2.0 1.0 7.0 1
296 2.0 1.0 3.0 1
[297 rows x 14 columns]
df = pd.read_csv('input.data', header=None, names=col_names);
age sex cp restbp chol fbs restecg thalach exang oldpeak slope ca thal num
-- ----- ----- ---- -------- ------ ----- --------- --------- ------- --------- ------- ---- ------ -----
0 63 1 1 145 233 1 2 150 0 2.3 3 0 6 0
1 67 1 4 160 286 0 2 108 1 1.5 2 3 3 1
2 67 1 4 120 229 0 2 129 1 2.6 2 2 7 1
3 37 1 3 130 250 0 0 187 0 3.5 3 0 3 0
4 41 0 2 130 204 0 2 172 0 1.4 1 0 3 0