Python Pandas 使用示例

使用Boolean 选择rows

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Emily'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'Paris', 'London', 'Tokyo']}
df = pd.DataFrame(data)
print(df)
print('================')

# List of boolean values
boolean_list = [True, False, True, False]

# Select rows based on the boolean list
selected_rows = df[boolean_list]
print(selected_rows)

输出：

    Name  Age      City
0   John   25  New York
1  Alice   30     Paris
2    Bob   35    London
3  Emily   40     Tokyo
================
   Name  Age      City
0  John   25  New York
2   Bob   35    London

读取Excel表格里指定的sheet, 并跳过起始n行

安装openpyxl, openpyxl是Python操作Excel文件的另一个库，可以非常方便地读写Excel文件的各种内容

pip install openpyxl

df = pd.read_excel(path, sheet_name= sheetName, skiprows=1)

• sheet_name： 指定读取某个sheet
• skiprows： 参数跳过起始的n行，因为可能有表头等合并单元格之类的数据

删除只有一个元素的行

假设excel表格正常的每行有4个数据(即4列)，但是某一行只有一个单元格有数据，需要删除该行（改行可能是数据或者其他内容）

df = df.dropna(thresh=2)  

• thresh： thresh = 2表示，如果一行至少有2个以上非空值时才会被删除

删除重复的

原始data frame

brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

默认删除，基于所有列删除重复行

df = df.drop_duplicates()

 brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

删除特定的重复列, 使用subset

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

keep参数的使用

keep{‘first’, ‘last’, False}, default ‘first’
Determines which duplicates (if any) to keep.
‘first’ : Drop duplicates except for the first occurrence.
‘last’ : Drop duplicates except for the last occurrence.

df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0

合并多个csv文件到excel表格中

使用前确保安装了xlsxwriter

pip install xlsxwriter

import os
import pandas as pd

# Get the directory name
directory_name = "your file path"

# List all CSV files in the directory
csv_files = [f for f in os.listdir(directory_name) if f.endswith('.csv')]

# Create a Pandas Excel writer using the directory name as the Excel file name
excel_file_name = os.path.basename(directory_name) + '.xlsx'
excel_writer = pd.ExcelWriter(excel_file_name, engine='xlsxwriter')

# Loop through each CSV file and add it as a new sheet in the Excel file
for csv_file in csv_files:
    sheet_name = os.path.splitext(csv_file)[0]  # Use CSV file name as sheet name
    # remove the special characters of the name, here is an example
    # sheet_name = sheet_name.replace("[", "").replace("]", "")  # Remove "[" and "]"
    df = pd.read_csv(os.path.join(directory_name, csv_file))
    df.to_excel(excel_writer, sheet_name=sheet_name, index=False)

# Save the Excel file
excel_writer.close()

print(f"CSV files merged into {excel_file_name} with different sheets.")

获取csv文件的数据

data = pd.read_csv('test.csv', header=None, encoding = "utf-8").values

读取后，data如下所示

可以通过索引去获取某一行某一列的数据

for i in range(len(data)):
	print(data[i][0])
	print(data[i][1])
	print(data[i][2])