使用 Pandas 从 CSV 中获取某些行和某些列

Question

我将如何查找列中的特定值，然后在 pandas 中选择该行中列值的子集？

我有一个 CSV，其中有一列包含美国各州名称和各州属性的列，但我只需要有关我正在查找的州的某些值。

例如，有 50 行（针对 50 个州）和 20 列包含有关每个州的各种数据，我想选择科罗拉多州和佛罗里达州，而这些州只有 5 个列值。

这是我要修改的代码：

import glob
import pandas as pd
import os
import csv
 
myList = []
 
path = "/path/to/source/files/*.csv"
 
for fname in glob.glob(path):
    df = pd.read_csv(fname)
    row = df.loc[df['Province_State'] == 'Pennsylvania']
 
    # Put the date in, derived from the CSV name
    dateFromFilename = os.path.basename(fname).replace('.csv','')
    row['Date'] = dateFromFilename
 
    myList.append(row)
    print(row)
 
concatList = pd.concat(myList, sort=True)
 
concatList.to_csv('/path/to/output.csv', index=False, header=True)

Answer 1

您只需要使用isin 并将列列表传递给loc：

myList = []

path = "/path/to/source/files/*.csv"

col_lists = ['col1','col2','col3']
 
for fname in glob.glob(path):
    df = pd.read_csv(fname)

    # changes here
    row = df.loc[df['Province_State'].isin('Florida', 'Colorado'),
                 col_list]

    # pivot
    row = (row.assign(idx=row.groupby('Province_State').cumcount()).     
              .pivot(index='idx', columns='Province_State')
          )
    # rename
    row.columns = [f'{x}_{y}' for x,y in row.columns]          

    # Put the date in, derived from the CSV name
    dateFromFilename = os.path.basename(fname).replace('.csv','')
    row['Date'] = dateFromFilename
 
    myList.append(row)
    print(row)
 
concatList = pd.concat(myList, sort=True)