我已将连续变量 x 转换为区间。我有一个带有数值的变量。数据框是：
data = {'x':[(-0.001, 7.0], (7.0, 19.0], (19.0, 97.0], (97.0, 817.0]],
'y':[769.0, 810.0,757.0,652.0]}
# Create DataFrame
df = pd.DataFrame(data)
df
这两个变量的数据类型都是float64. 此外，变量“x”的描述如下：
Name: x, dtype: category
Categories (4, interval[float64]): [(-0.001, 7.0] < (7.0, 19.0] < (19.0, 97.0] < (97.0, 817.0]]
现在，我使用绘图来绘制这两个变量之间的关系：
# figure
plot_data = [
go.Scatter(
x = df['x'],
y = df['y'])]
plot_layout = go.Layout(title=' Relationship between x and y')
fig = go.Figure(data=plot_data, layout=plot_layout)
pyoff.iplot(fig)
但显示的错误是：
TypeError: Object of type Interval is not JSON serializable …我的代码是：
import torch
from transformers import BertTokenizer
from IPython.display import clear_output
我在行中遇到错误from transformers import BertTokenizer：
ImportError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /mnt/home/wbj/anaconda3/envs/pytorch/lib/python3.8/site-packages/tokenizers/tokenizers.cpython-38-x86_64-linux-gnu.so)
我找到了一个答案，问题是由于文件引起的/lib/x86_64-linux-gnu/libm.so.6，当我使用代码时，strings /lib/x86_64-linux-gnu/libm.so.6 | grep GLIBC_我得到了输出
GLIBC_2.2.5
GLIBC_2.4
GLIBC_2.15
GLIBC_2.18
GLIBC_2.23
GLIBC_PRIVATE
该文件不支持 GLIBC_2.29。我该如何解决这个问题？
我创建了一个对我有用的函数，它将文件上传到 Google Cloud Storage。
问题是当我的朋友尝试使用他本地机器上的相同代码将同一个文件上传到同一个存储桶时，他收到timeout error。他的互联网非常好，他应该能够在他的连接中毫无问题地上传文件。
知道为什么会这样吗？
def upload_to_cloud(file_path):
"""
saves a file in the google storage. As Google requires audio files greater than 60 seconds to be saved on cloud before processing
It always saves in 'audio-files-bucket' (folder)
Input:
Path of file to be saved
Output:
URI of the saved file
"""
print("Uploading to cloud...")
client = storage.Client().from_service_account_json(KEY_PATH)
bucket = client.get_bucket('audio-files-bucket')
file_name = str(file_path).split('\\')[-1]
print(file_name)
blob = bucket.blob(file_name)
f = open(file_path, 'rb')
blob.upload_from_file(f)
f.close()
print("uploaded at: ", …我正在编写一个脚本,将一个"列"添加到500 Hz的Python列表中.以下是生成测试数据并通过单独线程传递的代码:
# fileA
import random, time, threading
data = [[] for _ in range(4)] # list with 4 empty lists (4 rows)
column = [random.random() for _ in data] # synthetic column of data
def synthesize_data():
while True:
for x,y in zip(data,column):
x.append(y)
time.sleep(0.002) # equivalent to 500 Hz
t1 = threading.Thread(target=synthesize_data).start()
# example of data
# [[0.61523098235, 0.61523098235, 0.61523098235, ... ],
# [0.15090349809, 0.15090349809, 0.15090349809, ... ],
# [0.92149878571, 0.92149878571, 0.92149878571, ... ],
# [0.41340918409, 0.41340918409, 0.41340918409, ... …