如何合并python中的两个不规则数据帧
如何合并python中的两个不规则数据帧
提问于 2018-06-04 10:35:19
所以我有两个数据帧
在第一个数据框架中，它显示了4辆汽车在不同的销售日期和销售数量。
在第二个数据帧中，有些汽车有修理功能，所以它们被称为
df1：
Cars Range(Days) Sold
0 A 1-3 5
1 4-7 23
2 8-15 2
3 B 4-7 4
4 8-15 1
5 C 1-3 5
6 D 1-3 2
7 E 1-3 9和
df2：
Car Repair_Calls
0 A 2
1 C 45
2 D 32
4 E 1我试过了
Df1‘修复_调用’=df2_2‘修复_调用’
我得到了什么
Car Range(Days) Sold Repair_Calls
0 A 1-3 5 2
1 4-7 23 45
2 8-15 2 32
3 B 4-7 4 1
4 8-15 1
5 C 1-3 5
6 D 1-3 2
7 E 1-3 9 预期产出
Car Range(Days) Sold Repair_Calls
0 A 1-3 5 2
1 4-7 23
2 8-15 2
3 B 4-7 4 0
4 8-15 1
5 C 1-3 5 45
6 D 1-3 2 32
7 E 1-3 9 1回答 2
Stack Overflow用户
回答已采纳
发布于 2018-06-04 10:37:25
使用map by Series，由set_index和df2创建
df1['Repair_Calls'] = df1['Cars'].map(df2.set_index('Car')['Repair_Calls'])或merge与left联接：
df1 = df1.merge(df2, left_on='Cars',right_on='Car', how='left').drop('Car', axis=1)
print (df1)
Cars Range(Days) Sold Repair_Calls
0 A 1-3 5 2.0
1 NaN 4-7 23 NaN
2 NaN 8-15 2 NaN
3 B 4-7 4 NaN
4 NaN 8-15 1 NaN
5 C 1-3 5 45.0
6 D 1-3 2 32.0
7 E 1-3 9 1.0但是，如果还需要添加缺失值，则通过唯一的非reindex值添加NaN：
s = df2.set_index('Car')['Repair_Calls'].reindex(df1['Cars'].dropna().unique(), fill_value=0)
df1['Repair_Calls'] = df1['Cars'].map(s)
print (df1)
Cars Range(Days) Sold Repair_Calls
0 A 1-3 5 2.0
1 NaN 4-7 23 NaN
2 NaN 8-15 2 NaN
3 B 4-7 4 0.0
4 NaN 8-15 1 NaN
5 C 1-3 5 45.0
6 D 1-3 2 32.0
7 E 1-3 9 1.0Stack Overflow用户
发布于 2018-06-04 11:50:18
@san，您可以尝试以下代码：
如果解决方案不能满足您的问题，请提供更多的输入和解释。
“法典”
import pandas as pd
import numpy as np
data_arr1 = [
['A', '1-3', 5],
['', '4-7', 23],
['', '8-15', 2],
['B', '4-7', 4],
['', '8-15', 1],
['C', '1-3', 5],
['D', '1-3', 2],
['E', '1-3', 9]
]
columns1 = ["Car", "Range(Days)", "Sold"];
data_arr2 = [
['A', 2],
['C', 45],
['D', 32],
['E', 1]
];
columns2 = ["Car", "Repair_Calls"];
# Creating DataFrames
df = pd.DataFrame(data_arr1, columns=columns1)
df2 = pd.DataFrame(data_arr2, columns=columns2)
# Printing Dataframes
print(df);
print('\n')
print(df2);
# Merging df & df2 to get desired output
df3 = pd.merge(left=df, right=df2, left_on="Car", right_on="Car", how="outer").replace(np.nan, "", regex=True)
print('\n')
print(df3)产出
Car Range(Days) Sold
0 A 1-3 5
1 4-7 23
2 8-15 2
3 B 4-7 4
4 8-15 1
5 C 1-3 5
6 D 1-3 2
7 E 1-3 9
Car Repair_Calls
0 A 2
1 C 45
2 D 32
3 E 1
Car Range(Days) Sold Repair_Calls
0 A 1-3 5 2
1 4-7 23
2 8-15 2
3 8-15 1
4 B 4-7 4
5 C 1-3 5 45
6 D 1-3 2 32
7 E 1-3 9 1页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/50678599
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