use_column_width=True
时间: 2023-08-05 18:10:29 浏览: 103
在 Streamlit 中,可以使用 `use_column_width=True` 参数将输出的元素宽度设置为当前列的宽度。这个参数适用于大多数的 Streamlit 元素,如 `st.write()`、`st.markdown()`、`st.code()` 等等。
例如,你可以使用以下代码将 `st.write()` 函数的输出宽度设置为当前列的宽度:
```python
import streamlit as st
st.write("Hello, world!", use_column_width=True)
```
在上面的示例中,`use_column_width=True` 参数告诉 Streamlit 将输出宽度设置为当前列的宽度。这样可以确保输出的元素宽度与应用程序的其他元素相匹配,使应用程序看起来更加整洁和一致。
相关问题
if sidebar == "店铺词云": st.title("店铺词云") 项目选择框 project_name = st.selectbox( "请选择想要的功能", ["显示店铺词云", "显示分析结果"] ) if project_name == "显示店铺词云": 这是一个进度条 bar = st.progress(0) for i in range(100): time.sleep(0.01) bar.progress(i) st.write("项目信息1:%s, 项目信息2:%s, 项目信息3:%s" % (project_info_1, project_info_2, project_info_3)) img = Image.open('D:\python练习\店铺词云.png') st.image(img, caption='店铺词云', use_column_width=True) st.balloons() st.success("显示成功") elif project_name == "显示分析结果": bar = st.progress(0) for i in range(100): time.sleep(0.01) bar.progress(i) st.text("1、由图可知,目前市面上大多网络手机壳店家主要用“旗舰店”、“数码”、“专营店”等作为店名,以此增加在消费者中的公信力。\n2、其中,“天天特卖工厂”淘宝官方的直营店之一,由于官方流量的扶持,权重比普通商家开设的店铺更高,因此在店铺中占有重要位置。\n3、手机壳的生产技术含量并不高,商家的门槛限制不高,大量商家涌入市场,且缺乏类似于行业龙头的商家进行引导或者模仿,手机壳商家的店铺名大多具有明显的个人色彩,因此上图出现了\n店铺名驳杂的情况。") st.balloons() st.success("显示成功")代码解析
这部分代码是在"店铺词云"选项下显示店铺词云或显示分析结果。
首先,使用st.selectbox()函数创建一个项目选择框,供用户选择想要的功能,包括"显示店铺词云"和"显示分析结果"。
接下来,使用条件语句判断用户选择的功能。如果选择了"显示店铺词云",则显示一个进度条,使用st.progress()函数创建一个进度条,并使用for循环和time.sleep()函数模拟进度条的更新,并使用st.write()函数显示项目信息。
然后,使用Image.open()函数打开名为"店铺词云.png"的图片文件,并使用st.image()函数显示图片,并使用st.balloons()函数显示气球效果,并使用st.success()函数显示成功提示信息。
如果选择了"显示分析结果",则同样显示一个进度条,并使用st.text()函数显示分析结果的文本信息,并使用st.balloons()函数显示气球效果,并使用st.success()函数显示成功提示信息。
在这段代码中,展示了在"店铺词云"选项下的两种功能的实现。如果你有其他问题,可以继续提问。
Read the stock price data of Alphabet Inc. (GOOG) for the time period between April 01, 2020 and October 01, 2020 in the file 'alphabet.csv' into a Pandas DataFrame. You can download it from Blackboard in session 12. (1) Convert the “Date” column into Datetime format and assign it as the index column. And output the DataFram containing data for month x. (2) Calculate the net increase of stock price for each day and append them as a new column “Net Increase” to the DataFrame using the closing price minus the opening price. And output the DataFram containing data for month x. (3) Find the day that has the highest opening price, and calculate the mean of closing price for all days. NOTE:You should add the following code to your program to ensure that all columns are displayed when printing.Otherwise, the system will consider it as incorrect. pd.set_option('display.max_columns',1000) pd.set_option('display.width', 1000) pd.set_option('display.max_colwidth',1000) Input An integer x.
Sure, here's a Python program that will solve the problem:
```python
import pandas as pd
# Read the CSV file into a Pandas DataFrame and convert the "Date" column to Datetime format
df = pd.read_csv('alphabet.csv')
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
# Define a function to calculate the net increase in stock price for each day
def calculate_net_increase(row):
return row['Close'] - row['Open']
# Add a new column to the DataFrame with the net increase for each day
df['Net Increase'] = df.apply(calculate_net_increase, axis=1)
# Get the input month from the user
x = int(input())
# Output the DataFrame for the input month, with all columns displayed
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
print("Data for month", x)
print(df[df.index.month == x])
# Find the day with the highest opening price and calculate the mean of the closing price for all days
highest_open = df['Open'].idxmax()
mean_close = df['Close'].mean()
print("Day with highest opening price:", highest_open.date())
print("Mean of closing price for all days:", mean_close)
```
To use this program, make sure that the 'alphabet.csv' file is in the same directory as your Python script. Then, simply run the program and input the desired month when prompted. The program will output the DataFrame containing data for the input month, as well as the day with the highest opening price and the mean of the closing price for all days.
Note that the program sets some display options for the Pandas DataFrame to ensure that all columns are displayed when printing. This is necessary to satisfy the requirements of the problem.