Hey there, let’s sort out that NaN problem with your long text columns in Excel. I’ve dealt with this exact scenario before when working with job descriptions and responsibilities, so here are practical fixes you can test right now:

• Explicitly define column data types
  Pandas often guesses data types automatically, and for extra-long text, it might misclassify columns leading to NaNs. Force the text columns to use the object dtype (which handles variable-length text perfectly):

  import pandas as pd
  
  # Swap these with your actual column names from the Excel file
  custom_dtypes = {'描述': 'object', '工作职责': 'object'}
  df = pd.read_excel("form1.xlsx", dtype=custom_dtypes)
  

• Use openpyxl with string dtype enforcement
  If you’re using openpyxl (the default engine for .xlsx files), you can tell pandas to read all columns as strings to avoid inference issues. This is a quick fix if you don’t mind converting all columns to string type:

  df = pd.read_excel("form1.xlsx", engine='openpyxl', dtype=str)
  

  Note: If you have numeric columns you need to keep as numbers, stick to the first method and only specify the text columns instead of applying str to everything.

• Clean hidden characters or empty cells post-import
  Sometimes NaNs show up because cells have hidden whitespace or control characters. After importing, you can clean up these columns to ensure no data is lost:

  # Replace NaNs with empty strings and strip hidden whitespace
  df['描述'] = df['描述'].fillna('').str.strip()
  df['工作职责'] = df['工作职责'].fillna('').str.strip()
  

  If some rows still look missing, double-check your Excel file—sometimes cells appear filled but have formula errors or are linked to empty cells.

• Check Excel’s cell formatting
  Ensure your text columns aren’t using a custom format that confuses pandas. Switching the column format from "General" to "Text" directly in Excel before importing can resolve misreading issues for super-long content.

Once you get all the data imported correctly, you’ll be all set to analyze those 150+ rows of job titles, descriptions, and responsibilities—happy analyzing!

内容的提问来源于stack exchange，提问作者Aj ml