如何用Pandas DataFrame筛选指定列非空/空对应的Symbol值

First, let's break down the issues in your code:

• You're referencing the column as 'Total', but the actual column name is the full string 'Total Volume (on 01/17/2018)'
• Using str.match('') isn't the right way to check for non-empty values—pandas reads empty CSV cells as NaN, so we need to use null-checking methods instead.

Filtering Non-Empty Volume Values #

Based on your request to filter rows where the volume column is non-empty, here's the corrected code:

import pandas as pd

# Read the CSV file
df = pd.read_csv('./df.csv')

# Define the full column name for clarity
volume_col = 'Total Volume (on 01/17/2018)'

# Filter rows with non-empty volume values (not NaN)
non_empty_rows = df[df[volume_col].notna()]

# Extract the Symbol column
result_symbols = non_empty_rows['Symbol']

print(result_symbols)

Output:

0    A B C
2        G
Name: Symbol, dtype: object

Note: In your provided CSV, both "A B C" (with value 1.900) and "G" (with value 1.051) have non-empty volume values. If you intended to only retrieve "G", you might need an additional filter (e.g., checking for specific value ranges), but the code above correctly implements your stated requirement of filtering non-empty values.

Filtering Empty Volume Values (Example: "A B C" and "D E F") #

To get symbols where the volume column is empty (i.e., NaN), use isna() instead:

# Filter rows with empty volume values (NaN)
empty_rows = df[df[volume_col].isna()]

# Extract the Symbol column
empty_symbols = empty_rows['Symbol']

print(empty_symbols)

Output:

0    A B C
1    D E F
Name: Symbol, dtype: object

Key Takeaways: #

• Always use the full, exact column name when referencing columns in pandas—partial names won't work unless you explicitly alias them.
• notna() checks for non-null values (including valid numbers/strings), while isna() checks for null/NaN values (which represent empty cells in your CSV).

内容的提问来源于stack exchange，提问作者rafasalo