將 header 添加到 Pandas 中的數據文件

Question

給定一個擴展名為.data的文件，我用pd.read_fwf("./input.data", sep=",", header = None)閱讀了它：

出去：

    0
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...

如何向其中添加以下列名稱？ 謝謝。

col_names = ["age", "sex", "cp", "restbp", "chol", "fbs", "restecg", 
           "thalach", "exang", "oldpeak", "slope", "ca", "thal", "num"]

更新：

pd.read_fwf("./input.data", names = col_names)

出去：

    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

Answer 1

如果檢查read_fwf ：

將固定寬度格式行的表格讀入 DataFrame。

因此，如果有分隔符,請使用read_csv ：

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]

Answer 2

只需在沒有read_csv的情況下執行 read_csv 並傳遞col_names ：

df = pd.read_csv('input.data', header=None, names=col_names);

Output（頭）：

      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