为什么在定义全局名称时出错？

Question

我试图使用python来计算答案here中的步骤之后的时间有序坐标之间的距离和速度。接近代码的末尾，我遇到一个错误，表示全局名称尚未定义但显然已经定义。

这是我的数据样本

    ID  timestamp           latitude        longitude
0   72  20/01/2015 09:47    -6.646405565    71.35696828
1   72  20/01/2015 15:47    -6.642237759    71.36032005
2   72  20/01/2015 21:47    -6.639229675    71.36914769
3   73  21/01/2015 03:47    -6.648699053    71.37865551
4   73  21/01/2015 09:47    -6.65574147     71.37957366
5   74  21/01/2015 15:47    -6.660118996    71.37990588
6   74  21/01/2015 21:47    -6.666138734    71.38266541

到目前为止，我已经能够运行以下代码

import pandas as pd
df = pd.read_csv(filename)  

df['timestamp'] = pd.to_datetime(df['timestamp'], format='%d/%m/%Y %H:%M')

from math import sin, cos, sqrt, atan2, radians

def getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2):
    R = 6371 # Radius of the earth in km
    dLat = radians(lat2-lat1)
    dLon = radians(lon2-lon1)
    rLat1 = radians(lat1)
    rLat2 = radians(lat2)
    a = sin(dLat/2) * sin(dLat/2) + cos(rLat1) * cos(rLat2) * sin(dLon/2) * sin(dLon/2) 
    c = 2 * atan2(sqrt(a), sqrt(1-a))
    d = R * c # Distance in km
    return d

def calc_velocity(dist_km, time_start, time_end):
    """Return 0 if time_start == time_end, avoid dividing by 0"""
    return dist_km / (time_end - time_start).seconds if time_end > time_start else 0

# First sort by ID and timestamp:
df = df.sort_values(by=['ID', 'timestamp'])

# Group the sorted dataframe by ID, and grab the initial value for lat, lon, and time.
df['lat0'] = df.groupby('ID')['latitude'].transform(lambda x: x.iat[0])
df['lon0'] = df.groupby('ID')['longitude'].transform(lambda x: x.iat[0])
df['t0'] = df.groupby('ID')['timestamp'].transform(lambda x: x.iat[0])

# create a new column for distance
df['dist_km'] = df.apply(
    lambda row: getDistanceFromLatLonInKm(
        lat1=row['latitude'],
        lon1=row['longitude'],
        lat2=row['lat0'],
        lon2=row['lon0']
    ),
    axis=1
)

在这一点上，我得到一个错误，暗示'getDistanceFromLatLonInKm'虽然已经定义但尚未定义。以下是回溯和错误

Traceback (most recent call last):
  File "<pyshell#36>", line 9, in <module>
    axis=1
  File "C:\Python27\ArcGIS10.6\lib\site-packages\pandas\core\frame.py", line 4061, in apply
    return self._apply_standard(f, axis, reduce=reduce)
  File "C:\Python27\ArcGIS10.6\lib\site-packages\pandas\core\frame.py", line 4157, in _apply_standard
    results[i] = func(v)
  File "<pyshell#36>", line 3, in <lambda>
    lambda row: getDistanceFromLatLonInKm(
NameError: ("global name 'getDistanceFromLatLonInKm' is not defined", u'occurred at index 0')

我在这个代码中哪里出错了？

Answer 1

如果您需要有关执行Python代码的不同方法的背景知识，请查看此链接。 https://realpython.com/run-python-scripts/

将以下代码复制粘贴到文件中，并将文件另存为lat_long.py。根据您的系统仅更改csv文件名'lat_long.csv'。在shell或命令提示符下，执行以下命令：

python lat_long.py。

python解释器将运行文件lat_long.py的内容并打印结果（如果有）。

import pandas as pd
from math import sin, cos, sqrt, atan2, radians

filename = 'lat_long.csv'
df = pd.read_csv(filename)


df['timestamp'] = pd.to_datetime(df['timestamp'], format='%d/%m/%Y %H:%M')


def getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2):
    R = 6371 # Radius of the earth in km
    dLat = radians(lat2-lat1)
    dLon = radians(lon2-lon1)
    rLat1 = radians(lat1)
    rLat2 = radians(lat2)
    a = sin(dLat/2) * sin(dLat/2) + cos(rLat1) * cos(rLat2) * sin(dLon/2) * sin(dLon/2)
    c = 2 * atan2(sqrt(a), sqrt(1-a))
    d = R * c # Distance in km
    return d

def calc_velocity(dist_km, time_start, time_end):
    """Return 0 if time_start == time_end, avoid dividing by 0"""
    return dist_km / (time_end - time_start).seconds if time_end > time_start else 0

# First sort by ID and timestamp:
df = df.sort_values(by=['ID', 'timestamp'])

# Group the sorted dataframe by ID, and grab the initial value for lat, lon, and time.
df['lat0'] = df.groupby('ID')['latitude'].transform(lambda x: x.iat[0])
df['lon0'] = df.groupby('ID')['longitude'].transform(lambda x: x.iat[0])
df['t0'] = df.groupby('ID')['timestamp'].transform(lambda x: x.iat[0])

# create a new column for distance
df['dist_km'] = df.apply(
    lambda row: getDistanceFromLatLonInKm(
        lat1=row['latitude'],
        lon1=row['longitude'],
        lat2=row['lat0'],
        lon2=row['lon0']
    ),
    axis=1
)
print(df)