如何通过列名获取列值？附示例数据集

Hey there, let's break down how to pull values from specific columns using their names for your dataset. First, here's your data formatted clearly for reference:

production_type type_a type_b type_c type_d
0 type_a 1.173783 0.714846 0.583621 1
1 type_b 1.418876 0.864110 0.705485 1
2 type_c 1.560452 0.950331 0.775878 1
3 type_d 1.750531 1.066091 0.870388 1
4 type_a 1.797883 1.094929 0.893932 1
5 type_a 1.461784 0.890241 0.726819 1
6 type_b 0.941938 0.573650 0.468344 1
7 type_a 0.507370 0.308994 0.252271 1
8 type_c 0.443565 0.270136 0.220547 1
9 type_d 0.426232 0.259579 0.211928 1
10 type_d 0.425379 0.259060 0.211504 1

Below are two common solutions depending on whether you want to use a dedicated data library or stick to pure Python:

Solution 1: Using Pandas (Most Common for Tabular Data) #

Pandas is the standard tool for working with structured data in Python. Here's how to implement this:

First, load your data into a DataFrame (you can also load it directly from a CSV file with pd.read_csv()):

import pandas as pd

# Define your data as a list of rows
data_rows = [
    ["type_a", 1.173783, 0.714846, 0.583621, 1],
    ["type_b", 1.418876, 0.864110, 0.705485, 1],
    ["type_c", 1.560452, 0.950331, 0.775878, 1],
    ["type_d", 1.750531, 1.066091, 0.870388, 1],
    ["type_a", 1.797883, 1.094929, 0.893932, 1],
    ["type_a", 1.461784, 0.890241, 0.726819, 1],
    ["type_b", 0.941938, 0.573650, 0.468344, 1],
    ["type_a", 0.507370, 0.308994, 0.252271, 1],
    ["type_c", 0.443565, 0.270136, 0.220547, 1],
    ["type_d", 0.426232, 0.259579, 0.211928, 1],
    ["type_d", 0.425379, 0.259060, 0.211504, 1]
]

# Define column names and create the DataFrame
column_names = ["production_type", "type_a", "type_b", "type_c", "type_d"]
df = pd.DataFrame(data_rows, columns=column_names)

Then extract a column by name using either bracket notation (works for all column names) or dot notation (only works if the column name has no spaces/special characters):

# Bracket notation (universal)
type_a_values = df["type_a"]
print(type_a_values)

# Dot notation (simpler for clean column names)
type_b_values = df.type_b
print(type_b_values)

If you want the values as a plain Python list instead of a Pandas Series, add .tolist():

type_c_list = df["type_c"].tolist()
print(type_c_list)

Solution 2: Pure Python (No External Libraries) #

If you prefer not to use Pandas, you can handle this with basic Python structures:

First, convert your data into a list of dictionaries where each dictionary maps column names to row values:

# Define header and row data
header = ["production_type", "type_a", "type_b", "type_c", "type_d"]
rows = [
    ["type_a", 1.173783, 0.714846, 0.583621, 1],
    ["type_b", 1.418876, 0.864110, 0.705485, 1],
    ["type_c", 1.560452, 0.950331, 0.775878, 1],
    ["type_d", 1.750531, 1.066091, 0.870388, 1],
    ["type_a", 1.797883, 1.094929, 0.893932, 1],
    ["type_a", 1.461784, 0.890241, 0.726819, 1],
    ["type_b", 0.941938, 0.573650, 0.468344, 1],
    ["type_a", 0.507370, 0.308994, 0.252271, 1],
    ["type_c", 0.443565, 0.270136, 0.220547, 1],
    ["type_d", 0.426232, 0.259579, 0.211928, 1],
    ["type_d", 0.425379, 0.259060, 0.211504, 1]
]

# Convert to list of dictionaries
data_dicts = [dict(zip(header, row)) for row in rows]

Then create a helper function to extract all values for a specific column:

def get_column_values(column_name, data):
    return [row[column_name] for row in data]

# Example: Get all values from the "type_d" column
type_d_values = get_column_values("type_d", data_dicts)
print(type_d_values)

Either approach will let you easily pull values by column name. Let me know if you need further clarification on any step!

内容的提问来源于stack exchange，提问作者Joe