Pandas 连接新列
Pandas Concat new column
为什么我在 'ACTION' 列中得到 NaN？
我得到那个结果对我来说似乎很奇怪。我试过使用 ignore_index = True 并且出现频率错误。
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 NaN
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 NaN
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 NaN
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 NaN
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 NaN
我想得到-
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
buy_stp = pd.Series([100,200,300,400,500],name= 'ACTION')
print(buy_stp)
df10 = pd.concat([df_concat_results,
buy_stp],
axis=1,
join_axes=[df_concat_results.index])
print(df10)
如果我没理解错的话，您只是想向数据框中添加一列。如果是这样，这是最简单的方法。
df['Action'] = buy_stp
您需要相同的索引 - Series 和 DataFrame 用于对齐，否则得到 NaNs:
buy_stp.index = df.index
df['ACTION'] = buy_stp
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
或者：
buy_stp = pd.Series([100,200,300,400,500],name= 'ACTION', index=df.index)
print(buy_stp)
datetime
2017-03-14 00:52:00 100
2017-03-13 23:54:00 200
2017-03-14 01:03:00 300
2017-03-14 00:03:00 400
2017-03-13 23:57:00 500
Name: ACTION, dtype: int64
df['ACTION'] = buy_stp
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
如果通过 values 或 list 转换为 numpy array 也有效，只需要相同的长度 df 和 buy_stp:
df['ACTION'] = buy_stp.values
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
df['ACTION'] = buy_stp.tolist()
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
为什么我在 'ACTION' 列中得到 NaN？ 我得到那个结果对我来说似乎很奇怪。我试过使用 ignore_index = True 并且出现频率错误。
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 NaN
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 NaN
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 NaN
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 NaN
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 NaN
我想得到-
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
buy_stp = pd.Series([100,200,300,400,500],name= 'ACTION')
print(buy_stp)
df10 = pd.concat([df_concat_results,
buy_stp],
axis=1,
join_axes=[df_concat_results.index])
print(df10)
如果我没理解错的话，您只是想向数据框中添加一列。如果是这样，这是最简单的方法。
df['Action'] = buy_stp
您需要相同的索引 - Series 和 DataFrame 用于对齐，否则得到 NaNs:
buy_stp.index = df.index
df['ACTION'] = buy_stp
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
或者：
buy_stp = pd.Series([100,200,300,400,500],name= 'ACTION', index=df.index)
print(buy_stp)
datetime
2017-03-14 00:52:00 100
2017-03-13 23:54:00 200
2017-03-14 01:03:00 300
2017-03-14 00:03:00 400
2017-03-13 23:57:00 500
Name: ACTION, dtype: int64
df['ACTION'] = buy_stp
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
如果通过 values 或 list 转换为 numpy array 也有效，只需要相同的长度 df 和 buy_stp:
df['ACTION'] = buy_stp.values
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500
df['ACTION'] = buy_stp.tolist()
print (df)
C H L O OI V WAP ACTION
datetime
2017-03-14 00:52:00 8.25 8.25 8.19 8.21 302.0 1769.0 8.22 100
2017-03-13 23:54:00 8.09 8.10 8.09 8.10 6.0 65.0 8.10 200
2017-03-14 01:03:00 8.29 8.32 8.28 8.29 175.0 1084.0 8.30 300
2017-03-14 00:03:00 8.15 8.15 8.14 8.15 13.0 50.0 8.15 400
2017-03-13 23:57:00 8.13 8.13 8.12 8.12 3.0 6.0 8.12 500