本文章主要通过Python完成NGSIM数据集按路段和车型的筛选，话不多说，直接上代码：
import pandas as pd
from pathlib import Path
datas = pd.read_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data.csv')
def main():
choice = int(input('请输入需要提取数据的路段(1.i-80;2.us-101;3.larkershim;4.peachtree)：'))
if choice==1:
i_80()
if choice==2:
us_101()
if choice==3:
larkershim()
if choice==4:
peachtree()
def i_80():
"""获取i-80道路上的数据"""
datas_i_80=datas[datas.Location=='i-80']
""""按车辆类型进行数据读取"""
answer=int(input('请输出需要提取的车辆类型数据（1.摩托车；2.小型车；3.大型车):'))
if answer==1:
datas_i_80_m=datas_i_80[datas_i_80.v_Class==1]
datas_i_80_m.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\I-80\\摩托车数据.csv')
print('i-80路段，摩托车数据已导出完毕')
if answer==2:
datas_i_80_c = datas_i_80[datas_i_80.v_Class == 2]
datas_i_80_c.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\I-80\\小型车数据.csv')
if datas_i_80_c.shape[0]>1048765:
split_i_80_c()
print('i-80路段，小型车数据已导出完毕')
if answer==3:
datas_i_80_t = datas_i_80[datas_i_80.v_Class == 3]
datas_i_80_t.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\I-80\\大型车数据.csv')
print('i-80路段，大型车数据已导出完毕')
quertion()
def us_101():
datas_us_101 = datas[datas.Location == 'us-101']
answer = int(input('请输出需要提取的车辆类型数据（1.摩托车；2.小型车；3.大型车):'))
if answer == 1:
datas_us_101_m = datas_us_101[datas_us_101.v_Class == 1]
datas_us_101_m.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\US-101\\摩托车数据.csv')
print('us-101路段，摩托车数据已导出完毕')
if answer == 2:
datas_us_101_c = datas_us_101[datas_us_101.v_Class == 2]
datas_us_101_c.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\US-101\\小型车数据.csv')
if datas_us_101_c.shape[0]>1048765:
split_us_101_c()
print('us-101路段，小型车数据已导出完毕')
if answer == 3:
datas_us_101_t = datas_us_101[datas_us_101.v_Class == 3]
datas_us_101_t.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\US-101\\大型车数据.csv')
print('us-101路段，大型车数据已导出完毕')
quertion()
def larkershim():
datas_lankershim = datas[datas.Location == 'lankershim']
answer = int(input('请输出需要提取的车辆类型数据（1.摩托车；2.小型车；3.大型车):'))
if answer == 1:
datas_lankershim_m = datas_lankershim[datas_lankershim.v_Class == 1]
datas_lankershim_m.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\lankershim\\摩托车数据.csv')
print('lankershim路段，摩托车数据已导出完毕')
if answer == 2:
datas_lankershim_c = datas_lankershim[datas_lankershim.v_Class == 2]
datas_lankershim_c.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\lankershim\\小型车数据.csv')
if datas_lankershim_c.shape[0]>1048765:
split_lankershim_c()
print('lankershim路段，小型车数据已导出完毕')
if answer == 3:
datas_lankershim_t = datas_lankershim[datas_lankershim.v_Class == 3]
datas_lankershim_t.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\lankershim\\大型车数据.csv')
print('lankershim路段，大型车数据已导出完毕')
quertion()
def peachtree():
datas_peachtree = datas[datas.Location == 'peachtree']
answer = int(input('请输出需要提取的车辆类型数据（1.摩托车；2.小型车；3.大型车):'))
if answer == 1:
datas_peachtree_m = datas_peachtree[datas_peachtree.v_Class == 1]
datas_peachtree_m.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\peachtree\\摩托车数据.csv')
print('peachtree路段，摩托车数据已导出完毕')
if answer == 2:
datas_peachtree_c = datas_peachtree[datas_peachtree.v_Class == 2]
datas_peachtree_c.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\peachtree\\小型车数据.csv')
if datas_peachtree_c.shape[0]>1048765:
split_peachtree_c()
print('peachtree路段，小型车数据已导出完毕')
if answer == 3:
datas_lankershim_t = datas_peachtree[datas_peachtree.v_Class == 3]
datas_lankershim_t.to_csv('D:\\BaiduNetdiskDownload\\NGSIM_Data\\peachtree\\大型车数据.csv')
print('peachtree路段，大型车数据已导出完毕')
quertion()
def split_lankershim_c():
res_file_path = Path(r'D:\\BaiduNetdiskDownload\\NGSIM_Data\\lankershim\\小型车数据.csv') # 仅修改待分割文件路径即可
split_size = 1000000
tar_dir = res_file_path.parent / ("split_" + res_file_path.name.split(".")[0])
if not tar_dir.exists():
tar_dir.mkdir()
print("创建文件夹\t" + str(tar_dir))
print("目标路径：\t" + str(tar_dir))
print("分割文件：\t" + str(res_file_path))
print("分割大小：\t" + "{:,}".format(split_size))
tmp = pd.read_csv(res_file_path, nrows=10)
columns = tmp.columns.to_list()
idx = 0
while (len(tmp) > 0):
start = 1 + (idx * split_size)
tmp = pd.read_csv(res_file_path,
header=None,
names=columns,
skiprows=start,
nrows=split_size)
if len(tmp) <= 0:
break
file_name = res_file_path.name.split(".")[0] + "_{}_{}".format(start, start + len(tmp)) + ".csv"
file_path = tar_dir / file_name
tmp.to_csv(file_path, index=False)
idx += 1
print(file_name + "\t切割成功")
def split_i_80_c():
res_file_path = Path(r'D:\\BaiduNetdiskDownload\\NGSIM_Data\\I-80\\小型车数据.csv') # 仅修改待分割文件路径即可
split_size = 1000000
tar_dir = res_file_path.parent / ("split_" + res_file_path.name.split(".")[0])
if not tar_dir.exists():
tar_dir.mkdir()
print("创建文件夹\t" + str(tar_dir))
print("目标路径：\t" + str(tar_dir))
print("分割文件：\t" + str(res_file_path))
print("分割大小：\t" + "{:,}".format(split_size))
tmp = pd.read_csv(res_file_path, nrows=10)
columns = tmp.columns.to_list()
idx = 0
while (len(tmp) > 0):
start = 1 + (idx * split_size)
tmp = pd.read_csv(res_file_path,
header=None,
names=columns,
skiprows=start,
nrows=split_size)
if len(tmp) <= 0:
break
file_name = res_file_path.name.split(".")[0] + "_{}_{}".format(start, start + len(tmp)) + ".csv"
file_path = tar_dir / file_name
tmp.to_csv(file_path, index=False)
idx += 1
print(file_name + "\t切割成功")
def split_peachtree_c():
res_file_path = Path(r'D:\\BaiduNetdiskDownload\\NGSIM_Data\\peachtree\\小型车数据.csv') # 仅修改待分割文件路径即可
split_size = 1000000
tar_dir = res_file_path.parent / ("split_" + res_file_path.name.split(".")[0])
if not tar_dir.exists():
tar_dir.mkdir()
print("创建文件夹\t" + str(tar_dir))
print("目标路径：\t" + str(tar_dir))
print("分割文件：\t" + str(res_file_path))
print("分割大小：\t" + "{:,}".format(split_size))
tmp = pd.read_csv(res_file_path, nrows=10)
columns = tmp.columns.to_list()
idx = 0
while (len(tmp) > 0):
start = 1 + (idx * split_size)
tmp = pd.read_csv(res_file_path,
header=None,
names=columns,
skiprows=start,
nrows=split_size)
if len(tmp) <= 0:
break
file_name = res_file_path.name.split(".")[0] + "_{}_{}".format(start, start + len(tmp)) + ".csv"
file_path = tar_dir / file_name
tmp.to_csv(file_path, index=False)
idx += 1
print(file_name + "\t切割成功")
def split_us_101_c():
res_file_path = Path(r'D:\\BaiduNetdiskDownload\\NGSIM_Data\\US-101\\小型车数据.csv') # 仅修改待分割文件路径即可
split_size = 1000000
tar_dir = res_file_path.parent / ("split_" + res_file_path.name.split(".")[0])
if not tar_dir.exists():
tar_dir.mkdir()
print("创建文件夹\t" + str(tar_dir))
print("目标路径：\t" + str(tar_dir))
print("分割文件：\t" + str(res_file_path))
print("分割大小：\t" + "{:,}".format(split_size))
tmp = pd.read_csv(res_file_path, nrows=10)
columns = tmp.columns.to_list()
idx = 0
while (len(tmp) > 0):
start = 1 + (idx * split_size)
tmp = pd.read_csv(res_file_path,
header=None,
names=columns,
skiprows=start,
nrows=split_size)
if len(tmp) <= 0:
break
file_name = res_file_path.name.split(".")[0] + "_{}_{}".format(start, start + len(tmp)) + ".csv"
file_path = tar_dir / file_name
tmp.to_csv(file_path, index=False)
idx += 1
print(file_name + "\t切割成功")
def quertion():
question=int(input('是否继续导出数据？(1.是；2.否）：'))
if question==1:
main()
if question==2:
print('程序关闭')
if __name__ == '__main__':
main()
Excel中仅能显示1048765行数据，split_lankershim_c()，split_i_80_c()，split_peachtree_c()，split_us_101_c()四个函数对数据进行切割，以便于将最后筛选的数据在Excel中进行显示。
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