Python 玩转数据 16 - Pandas 数据处理 追加 df.append()
引言
本文主要介绍 pandas 数据追加 df.append()，更多 Python 进阶系列文章，请参考 Python 进阶学习 玩转数据系列
内容提要：
df1.append()
追加 DataFrame
追加 Series
追加 dict 字典
df1.append()
按行追加拼接，将一个 DataFrame 的行拼接到另外一个 DataFrame 的末尾，返回一个新的 DataFrame。如果列名不在第一个 DataFrame出现，则将以新的列名添加，没有对应内容的会为空。不会改变原来的 DataFrame，只会创建一个新的 DataFrame，包含拼接的数据。
重点：因为会创建一个新的 index 和 data buffer，所以效率不高。推荐用 pd.concat()，而且 pd.concat() 功能更强大，详情请参考 Pandas 数据处理 拼接 pd.concat()
df.append(other: DataFrame | Series[Dtype@append] | Dict[_str, Any], ignore_index: _bool = …, verify_integrity: _bool = …, sort: _bool = …)
| 参数 | 说明 |
|---|---|
| other | 是它要追加的其他 DataFrame 或者类似序列内容 |
| ignore_index | 如果为 True 则重新进行自然索引 |
| verify_integrity | 如果为 True 则遇到重复索引内容时报错 |
| sort | 进行排序 |
追加 DataFrame
两个 df 拼接，并保留原索引：
多个 df 拼接，在合并不保留原索引，启用新的自然索引
代码：
import pandas as pd
idnumber = [1,2,5]
fname = ['Kate','John','Eli']
age = [10,20,50]
grade = ['A','B','C']
df1 = pd.DataFrame({'id':idnumber,'fname':fname})
df2 = pd.DataFrame({'age':age,'grade':grade})
df3 = pd.DataFrame({'id':[3,4],'age':[30,40]})
print("df1:\n{}".format(df1))
print("df2:\n{}".format(df2))
print("df3:\n{}".format(df3))
df1_append_df2 = df1.append(df2)
print("df1_append_df2:\n{}".format(df1_append_df2))
df1_append_df2_df3 = df1.append([df2,df3], ignore_index = True)
print("df1_append_df2_df3:\n{}".format(df1_append_df2_df3))
输出：
df1:
id fname
0 1 Kate
1 2 John
2 5 Eli
df2:
age grade
0 10 A
1 20 B
2 50 C
df3:
id age
0 3 30
1 4 40
df1_append_df2:
id fname age grade
0 1.0 Kate NaN NaN
1 2.0 John NaN NaN
2 5.0 Eli NaN NaN
0 NaN NaN 10.0 A
1 NaN NaN 20.0 B
2 NaN NaN 50.0 C
df1_append_df2_df3:
id fname age grade
0 1.0 Kate NaN NaN
1 2.0 John NaN NaN
2 5.0 Eli NaN NaN
3 NaN NaN 10.0 A
4 NaN NaN 20.0 B
5 NaN NaN 50.0 C
6 3.0 NaN 30.0 NaN
7 4.0 NaN 40.0 NaN
追加 Series
拼接 Series 一定要加上参数 ignore_index=True
Series 的 index 会分别对应列名
代码：
import pandas as pd
idnumber = [1,2,5]
fname = ['Kate','John','Eli']
df1 = pd.DataFrame({'id':idnumber,'fname':fname})
s1 = pd.Series(["aa", "bb"], index=["a", "b"])
s2 = pd.Series(["110", "Supermen"], index=["id", "fname"])
print("df1:\n{}".format(df1))
print("s1:\n{}".format(s1))
print("s2:\n{}".format(s2))
df1_appened_s1 = df1.append(s1, ignore_index=True)
df1_appened_s2 = df1.append(s2, ignore_index=True)
print("df1_appened_s1:\n{}".format(df1_appened_s1))
print("df1_appened_s2:\n{}".format(df1_appened_s2))
输出：
df1:
id fname
0 1 Kate
1 2 John
2 5 Eli
s1:
a aa
b bb
dtype: object
s2:
id 110
fname Supermen
dtype: object
df1_appened_s1:
id fname a b
0 1.0 Kate NaN NaN
1 2.0 John NaN NaN
2 5.0 Eli NaN NaN
3 NaN NaN aa bb
df1_appened_s2:
id fname
0 1 Kate
1 2 John
2 5 Eli
3 110 Supermen
追加 dict 字典
代码：
import pandas as pd
idnumber = [1,2,5]
fname = ['Kate','John','Eli']
df1 = pd.DataFrame({'id':idnumber,'fname':fname})
dicts_1 =[{"a":"aa", "b":"bb"},{"a":"aaa", "b":"bbb"}]
dicts_2 =[{"id":"110", "fname":"Supermen"},{"id":"111", "fname":"Superwoman"}]
print("df1:\n{}".format(df1))
print("dicts_1:\n{}".format(dicts_1))
print("dicts_1:\n{}".format(dicts_1))
df1_appened_dicts_1 = df1.append(dicts_1)
df1_appened_dicts_2 = df1.append(dicts_2, ignore_index=True)
print("df1_appened_dicts_1:\n{}".format(df1_appened_dicts_1))
print("df1_appened_dicts_2:\n{}".format(df1_appened_dicts_2))
输出：
df1:
id fname
0 1 Kate
1 2 John
2 5 Eli
dicts_1:
[{'a': 'aa', 'b': 'bb'}, {'a': 'aaa', 'b': 'bbb'}]
dicts_1:
[{'a': 'aa', 'b': 'bb'}, {'a': 'aaa', 'b': 'bbb'}]
df1_appened_dicts_1:
id fname a b
0 1.0 Kate NaN NaN
1 2.0 John NaN NaN
2 5.0 Eli NaN NaN
0 NaN NaN aa bb
1 NaN NaN aaa bbb
df1_appened_dicts_2:
id fname
0 1 Kate
1 2 John
2 5 Eli
3 110 Supermen
4 111 Superwoman