Python 基于另一个数据帧的列值打印一个数据帧的列值
Python 基于另一个数据帧的列值打印一个数据帧的列值,python,pandas,numpy,matplotlib,math,Python,Pandas,Numpy,Matplotlib,Math,这是我的第一个数据帧df1。在列中,开始DOY和结束DOY,例如3.0和6.0,我想通过将另一个数据帧df2的列值与列DOY匹配来打印该列值Bz、Vsw等。下面是一个简单的教程,介绍如何执行此操作: from pandas import DataFrame if __name__ == '__main__': data1 = {'Starting DOY': [3.0, 3.0, 13.0], 'Ending DOY': [6.0, 6.0, 15.0]}
这是我的第一个数据帧df1。在列中,开始DOY和结束DOY,例如3.0和6.0,我想通过将另一个数据帧df2的列值与列DOY匹配来打印该列值Bz、Vsw等。下面是一个简单的教程,介绍如何执行此操作:
from pandas import DataFrame
if __name__ == '__main__':
data1 = {'Starting DOY': [3.0, 3.0, 13.0],
'Ending DOY': [6.0, 6.0, 15.0]}
data2 = {'YEAR': [1975, 1975, 1975],
'DOY': [1.0, 3.0, 6.0],
'HR': [0, 1, 2],
'By': [-7.5, -4.0, -3.6],
'Bz': [0.2, 2.4, -2.3],
'Nsw': [999.9, 6.2, 5.9],
'Vsw': [9999.0, 476.0, 482.0],
'AE': [181, 138, 86]}
df1 = DataFrame(data1, columns=['Starting DOY',
'Ending DOY'])
df2 = DataFrame(data2, columns=['YEAR', 'DOY',
'HR', 'By', 'Bz',
'Nsw', 'Vsw', 'AE'])
for doy in df1.values:
start_doy = doy[0]
end_doy = doy[1]
for val in df2.values:
year = val[0]
current_doy = val[1]
hr = val[2]
By = val[3]
Bz = val[4]
Nsw = val[5]
Vsw = val[6]
AE = val[7]
if start_doy <= current_doy <= end_doy:
print("For DOY {}".format(current_doy))
print("By: {}".format(By))
print("Bz: {}".format(Bz))
print("Vsw: {}".format(Vsw))
print("--------------------")
下面是一个简单的教程,介绍如何进行此操作:
from pandas import DataFrame
if __name__ == '__main__':
data1 = {'Starting DOY': [3.0, 3.0, 13.0],
'Ending DOY': [6.0, 6.0, 15.0]}
data2 = {'YEAR': [1975, 1975, 1975],
'DOY': [1.0, 3.0, 6.0],
'HR': [0, 1, 2],
'By': [-7.5, -4.0, -3.6],
'Bz': [0.2, 2.4, -2.3],
'Nsw': [999.9, 6.2, 5.9],
'Vsw': [9999.0, 476.0, 482.0],
'AE': [181, 138, 86]}
df1 = DataFrame(data1, columns=['Starting DOY',
'Ending DOY'])
df2 = DataFrame(data2, columns=['YEAR', 'DOY',
'HR', 'By', 'Bz',
'Nsw', 'Vsw', 'AE'])
for doy in df1.values:
start_doy = doy[0]
end_doy = doy[1]
for val in df2.values:
year = val[0]
current_doy = val[1]
hr = val[2]
By = val[3]
Bz = val[4]
Nsw = val[5]
Vsw = val[6]
AE = val[7]
if start_doy <= current_doy <= end_doy:
print("For DOY {}".format(current_doy))
print("By: {}".format(By))
print("Bz: {}".format(Bz))
print("Vsw: {}".format(Vsw))
print("--------------------")
我认为最简单的方法是:
>>> df1 = pd.DataFrame([[1,2],[3,4],[5,6]], columns=["Starting DOY", "Ending DOY"])
>>> df2 = pd.DataFrame([[6,5,8, 1.5],[4,3,9, 3.5],[2,1,5, 5.5]], columns=["By", "Bz", "Vsw", "DOY"])
>>> df1.apply(lambda row: df2[(df2['DOY'] >= row[0]) & (df2['DOY'] <= row[1])], axis=1)
0 By Bz Vsw DOY
0 6 5 8 1.5
1 By Bz Vsw DOY
1 4 3 9 3.5
2 By Bz Vsw DOY
2 2 1 5 5.5
dtype: object
>df1=pd.DataFrame([[1,2]、[3,4]、[5,6]],columns=[“起始DOY”、“结束DOY”])
>>>df2=pd.DataFrame([[6,5,8,1.5],[4,3,9,3.5],[2,1,5,5.5]),列=[“By”,“Bz”,“Vsw”,“DOY”])
>>>df1.apply(lambda行:df2[(df2['DOY']>=row[0])&(df2['DOY']]我认为最简单的方法是:
>>> df1 = pd.DataFrame([[1,2],[3,4],[5,6]], columns=["Starting DOY", "Ending DOY"])
>>> df2 = pd.DataFrame([[6,5,8, 1.5],[4,3,9, 3.5],[2,1,5, 5.5]], columns=["By", "Bz", "Vsw", "DOY"])
>>> df1.apply(lambda row: df2[(df2['DOY'] >= row[0]) & (df2['DOY'] <= row[1])], axis=1)
0 By Bz Vsw DOY
0 6 5 8 1.5
1 By Bz Vsw DOY
1 4 3 9 3.5
2 By Bz Vsw DOY
2 2 1 5 5.5
dtype: object
>df1=pd.DataFrame([[1,2]、[3,4]、[5,6]],columns=[“起始DOY”、“结束DOY”])
>>>df2=pd.DataFrame([[6,5,8,1.5],[4,3,9,3.5],[2,1,5,5.5]),列=[“By”,“Bz”,“Vsw”,“DOY”])
>>>df1.apply(lambda row:df2[(df2['DOY']>=row[0])&(df2['DOY'])顺便说一句,欢迎来到社区。请不要发布图像;发布数据和代码,这样我们可以轻松地复制问题。顺便说一句,欢迎来到社区。请不要发布图像;发布数据和代码,这样我们可以轻松地复制问题。