将CSV/Excel中的多个表转换为Python中的字典或数据帧

我需要帮助

我有一个Excel文件，其中包含我试图进入数据框的数据，但数据是以表格形式存在的，不容易处理。例如：

我希望最终将其放入这种形式的数据框架中：

Meal               Food                              Calories
Breakfast          English Muffins                   120
Breakfast          Peanut Butter Spread              190
Morning Snack      Banana                            90
Morning Snack      Nectarine                         59
...                ...                               ...

以及此表单中每日总计的单独数据框（暂时忽略“日期”列）：

我正在努力将其放入数据帧中。查看数据集的屏幕截图，首先将数据存储到字典中是有意义的，但这会给我带来一组NA值，因为所有的空白单元格。

我想用我想要的方式来获取“膳食”专栏，就是做一个正向填充，但这意味着我将不得不使用一个系列或数据帧，而我还没有做到这一点

这就是我目前拥有的：

df = pd.read_excel('filename.xls', 'Foods')

# create a list to store the dictionaries
food_logs = []

# this is code to reformat the string values in a certain column 
# to get the name of the sheets I need to use in the Excel. This can be ignored
for day in df.values:
    if day[1] != '0':
        foodLogSheetName = 'Food Log ' + day[0].replace('-', '')
        food_logs.append(foodLogSheetName)

# 'foods' is now a list of nested dictionaries (think of everything in the 
# first screenshot as the outer dictionary, and each of the column as the 
# inner dictionary)
foods = [xls.parse(food_log).to_dict() for food_log in food_logs]

这就是“食品”目前的含义，如果我在每个外部字典之间用一行字打印出来：

我可以选择使用CSV文件，但是如果有意义的话，我可以选择垂直堆叠多个“表”，而不是多张图纸

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我将非常感谢任何人能提供的任何提示，请

我认为你的数据收集是正确的。听起来您可能只是在处理丢失的数据时遇到了问题。从您发布的示例中，看起来您可以将整个内容读入一个数据框，删除所有空行，在“用餐”列中添加F，然后删除任何部分为空的行（或子集上的行）

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有了你的建议，我才知道如何回答我的问题！非常感谢你！！

df = pd.read_excel('filename.xls', 'Foods')

# create a list to store the dictionaries
food_logs = []

# this is code to reformat the string values in a certain column 
# to get the name of the sheets I need to use in the Excel. This can be ignored
for day in df.values:
    if day[1] != '0':
        foodLogSheetName = 'Food Log ' + day[0].replace('-', '')
        food_logs.append(foodLogSheetName)

# 'foods' is now a list of nested dictionaries (think of everything in the 
# first screenshot as the outer dictionary, and each of the column as the 
# inner dictionary)
foods = [xls.parse(food_log).to_dict() for food_log in food_logs]

import pandas as pd

df = pd.read_excel(file_path_or_buffer, sheet_name=my_sheet_name, **other_kwargs)
# You should have a dataframe that looks like
# Meal               Food                              Calories
# Breakfast          
#                    English Muffins                   120
#                    Peanut Butter Spread              190
# ...
# Next drop totally NaN/empty rows
df.dropna(how='all', inplace=True)
df['Meal'] = df['Meal'].fillna(method='ffill')
# Now you should have something that looks like
# Meal               Food                              Calories
# Breakfast          
# Breakfast          English Muffins                   120
# Breakfast          Peanut Butter Spread              190
# ...
# Drop empty rows, if you need to allow for some sparse data, use the subset argument
df.dropna(how='any', inplace=True)