[pandas.concat] 纵向合并多个dataframe #pandas.concat #pandas

不同列名的纵向合并

# pandas.concat 实现竖向的拼接
import pandas as pd
df1=pd.DataFrame([10,12,13])
df2=pd.DataFrame([22,33,44,55])
df3=pd.DataFrame([90,94])
pd.concat([df1,df2,df3],ignore_index=True) # 纵向组合，注意此时没有设定列名。此时拼接只有一列

# --------concat 多列进行拼接---------
import pandas as pd
df1=pd.DataFrame([10,12,13],columns=['1'])
df2=pd.DataFrame([22,33,44,55],columns=['2'])
df3=pd.DataFrame([90,94],columns=['3'])
df = pd.concat([df1,df2,df3],ignore_index=True,)
df
# 此时按照有3列，结果如下
	1	2	3
0	10.0	NaN	NaN
1	12.0	NaN	NaN
2	13.0	NaN	NaN
3	NaN	22.0	NaN
4	NaN	33.0	NaN
5	NaN	44.0	NaN
6	NaN	55.0	NaN
7	NaN	NaN	90.0
8	NaN	NaN	94.0

参考资料：
https://notebook.community/xlbaojun/Note-jupyter/05%E5%85%B6%E4%BB%96/pandas%E6%96%87%E6%A1%A3-zh-master/%E6%95%B0%E6%8D%AE%E5%90%88%E5%B9%B6%E3%80%81%E8%BF%9E%E6%8E%A5%E5%92%8C%E6%8B%BC%E6%8E%A5-Merge,%20join,%20and%20concat

实现不同列名的dataframe纵向连接

   # 重命名为一个列名，并且忽略index
   df = pd.Dataframe(columns=['datetime','datetime_nano',f'{self.code}.last_price])
   df_file.columns = df.columns
   df = pd.concat([df,df_file],ignore_index=True)
   # 这是股票高频数据本地获取的逻辑