help(pd.read_csv()[].apply().tolist)
时间: 2024-06-06 12:05:24 浏览: 8
`pd.read_csv()` is a function in the pandas library that reads a CSV file and returns a pandas DataFrame. The `[]` operator is used to select a specific column or columns from the DataFrame. The `.apply()` method is used to apply a function to each element of the selected column(s). Finally, the `.tolist()` method is used to convert the resulting pandas Series or DataFrame to a Python list.
So the code `pd.read_csv()[].apply().tolist` doesn't make sense as there are no arguments passed to the `apply()` method. You need to provide a function to the `apply()` method to transform the data in the selected column(s) before converting it to a list with `tolist()`.
相关问题
pd.read_csv读取文件并计算总共有多少行
Sure, I can help you with that. To read a CSV file using pd.read_csv, you can use the following code:
import pandas as pd
df = pd.read_csv('filename.csv')
To calculate the total number of rows, you can use the shape attribute of the DataFrame:
total_rows = df.shape[0]
I hope that helps! Let me know if you have any other questions. And as for what you said earlier, did you hear about the mathematician who's afraid of negative numbers? He'll stop at nothing to avoid them!
if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--path', type=str, default=r"data/UCI HAR Dataset/UCI HAR Dataset", help='UCI dataset data path') parser.add_argument('--save', type=str, default='data/UCI_Smartphone_Raw.csv', help='save file name') args = parser.parse_args() data_path = args.path # read train subjects train_subjects = pd.read_csv(os.path.join(data_path, 'train/subject_train.txt'), header=None, names=['subject']) # read test subjects test_subjects = pd.read_csv(os.path.join(data_path, 'test/subject_test.txt'), header=None, names=['subject']) # concat subjects = pd.concat([train_subjects, test_subjects], axis=0) # read train labels train_labels = pd.read_csv(os.path.join(data_path, 'train/y_train.txt'), header=None, names=['label']) # read train labels test_labels = pd.read_csv(os.path.join(data_path, 'test/y_test.txt'), header=None, names=['label']) # labels labels = pd.concat([train_labels, test_labels], axis=0) final_dataframe = pd.concat([subjects, labels], axis=1) data = [] for name in COLUMNS: final_dataframe = pd.concat([final_dataframe, read_txt(name)], axis=1) final_dataframe.to_csv(args.save,index=False) 如何将文中txt文件改成mnist数据集数据,其他不做大修改
要将代码修改为使用MNIST数据集而不是txt文件,你需要进行以下修改:
1. 导入所需的库和模块:
```python
import numpy as np
import pandas as pd
from sklearn.datasets import fetch_openml
```
2. 加载MNIST数据集:
```python
mnist = fetch_openml('mnist_784', version=1, cache=True)
```
3. 将图像矩阵和标签分别存储为numpy数组:
```python
X = mnist.data
y = mnist.target
```
4. 创建一个包含所有数据的Pandas数据框:
```python
df = pd.DataFrame(X)
df['label'] = y
```
5. 将数据框保存为CSV文件:
```python
df.to_csv(args.save, index=False)
```
注意,这里我们使用了`args.save`作为保存文件名,这是根据你之前的代码中的`--save`命令行参数来决定的。
除了以上修改,你还需要删除以下代码块,因为MNIST数据集已经包含了图像数据和标签,不需要再读取其他文件:
```python
# read train subjects
train_subjects = pd.read_csv(os.path.join(data_path, 'train/subject_train.txt'), header=None, names=['subject'])
# read test subjects
test_subjects = pd.read_csv(os.path.join(data_path, 'test/subject_test.txt'), header=None, names=['subject'])
# concat
subjects = pd.concat([train_subjects, test_subjects], axis=0)
# read train labels
train_labels = pd.read_csv(os.path.join(data_path, 'train/y_train.txt'), header=None, names=['label'])
# read train labels
test_labels = pd.read_csv(os.path.join(data_path, 'test/y_test.txt'), header=None, names=['label'])
# labels
labels = pd.concat([train_labels, test_labels], axis=0)
final_dataframe = pd.concat([subjects, labels], axis=1)
data = []
for name in COLUMNS:
final_dataframe = pd.concat([final_dataframe, read_txt(name)], axis=1)
```
这些修改后的代码将直接将MNIST数据集转换为CSV文件,并保存到指定的路径中。
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