python - Pandas 数据框 - 使用一条记录创建数据框

Question

我想创建一个数据框而不从 CSV 读取它。

例如，我想创建列和一条记录。请假设这样的事情:

    Feature1 Feature 2  Feature 3 ... Feature n
1     20      False        3.2          True

我构建了一个分类器，我想做出预测: 分类器.预测(数据框)

我收到的记录是在特征之间带有“，”的字符串。我使用 split 来提取特征列表:

record_features = "16,713,Danny, ..."
features = record_features.split(',')

之后我将列表转换为系列:

series = pd.Series(features)

之后我想创建一个数据框: column_names = ['feature1', 'feature2', ..., 'feature102']

 df = pd.DataFrame(series, columns=column_names)

我收到一个错误:

ValueError: Shape of passed values is (1, 102), indices imply (102, 102)

我确实有 102 个功能，我想创建一个包含列的数据框和一条记录

有什么建议吗？

Answer 1

您可以添加[]:

column_names = ['Feature1','Feature2','Feature102']
record_features = "16,713,Danny"
features = record_features.split(',')

df = pd.DataFrame([features], columns=column_names)
print (df)
  Feature1 Feature2 Feature102
0       16      713      Danny

另一个 numpy 解决方案 reshape :

df = pd.DataFrame(np.array(features)
                    .reshape(len(features) // len(column_names), len(column_names)), 
                 columns=column_names)
print (df)
  Feature1 Feature2 Feature102
0       16      713      Danny

时间:

column_names = ['Feature' + str(x) for x in range(102)]
record_features = "16,713,Danny"
features = record_features.split(',')
features = features * 34

In [222]: %timeit pd.DataFrame([features], columns=column_names)
100 loops, best of 3: 5.94 ms per loop

In [223]: %timeit pd.DataFrame(dict(zip(column_names, features)), index=[0], columns=column_names)
The slowest run took 4.48 times longer than the fastest. This could mean that an intermediate result is being cached.
100 loops, best of 3: 5.25 ms per loop

In [224]: %timeit pd.DataFrame(np.array(features).reshape(len(features) // len(column_names), len(column_names)), columns=column_names)
The slowest run took 5.60 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 206 µs per loop