DataFrame数据值遍历访问方法
DataFrame数据值遍历访问方法
遍历查询或修改dataframe的元素值，通常使用loc和iloc函数实现定位元素，对比在遍历应用中，loc和iloc的使用方法和区别。
1. loc和iloc使用
loc函数：通过行索引 “Index” 中的具体值来取行数据（按column名访问，或者通过条件访问）
iloc函数：通过行号来取行数据（按行号和列号访问，不能用列名访问）
注：loc是location的意思，iloc中的i是integer的意思，仅接受整数作为参数。
loc官网说明，主要用于列标签访问
.loc is primarily label based, but may also be used with a boolean
array.
- A single label, e.g. 5 or ‘a’ (Note that 5 is interpreted as a label of the index. This use is not an integer position along the index.).
- A list or array of labels [‘a’, ‘b’, ‘c’].
- A slice object with labels ‘a’:‘f’ (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index! See Slicing with labels and Endpoints are inclusive.)
- A boolean array (any NA values will be treated as False).
- A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).
iloc官网说明，主要基于整数位置访问。
.iloc is primarily integer position based (from 0 to length-1 of the
axis), but may also be used with a boolean array. .iloc will raise
IndexError if a requested indexer is out-of-bounds, except slice
indexers which allow out-of-bounds indexing. (this conforms with
Python/NumPy slice semantics). Allowed inputs are:
- An integer e.g. 5.
- A list or array of integers [4, 3, 0].
- A slice object with ints 1:7.
- A boolean array (any NA values will be treated as False).
- A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).
2.loc数据值遍历
（1）基于日期索引
#日期做索引
df = pd.DataFrame(abs(np.random.randn(10, 4)), index=pd.date_range('1/1/2023', periods=10),
columns=list('ABCD'))
df.index.name='date'
i = 1
#按日期索引遍历
for d in df.index :
#偶数行A列赋值0
if i%2 ==0 :
df.loc[df.index==d,'A'] = 0
i += 1
#索引列值最小值A列赋值100
df.loc[df.index==df.index.min() ,['A']]=100
print(df)
pre=100
#A列如果是0 ，向下赋值
for d in df.index :
if df.loc[d,'A']== 0.0 :
df.loc[d,'A']=pre
pre=df.loc[d,'A']
print(df)
效果如下：
A B C D
date
2023-01-01 100.000000 1.532303 1.667700 0.799870
2023-01-02 0.000000 0.183089 1.239692 1.321370
2023-01-03 0.741102 0.046467 1.132106 0.019921
2023-01-04 0.000000 1.051709 0.236322 1.521744
2023-01-05 0.385593 0.533345 0.762100 0.654683
2023-01-06 0.000000 1.244160 0.433445 1.050108
2023-01-07 0.243023 2.255967 0.165955 0.287973
2023-01-08 0.000000 0.454799 1.382565 0.732341
2023-01-09 0.497839 0.371737 0.366683 0.524772
2023-01-10 0.000000 0.677077 0.542580 1.384272
A B C D
date
2023-01-01 100.000000 1.532303 1.667700 0.799870
2023-01-02 100.000000 0.183089 1.239692 1.321370
2023-01-03 0.741102 0.046467 1.132106 0.019921
2023-01-04 0.741102 1.051709 0.236322 1.521744
2023-01-05 0.385593 0.533345 0.762100 0.654683
2023-01-06 0.385593 1.244160 0.433445 1.050108
2023-01-07 0.243023 2.255967 0.165955 0.287973
2023-01-08 0.243023 0.454799 1.382565 0.732341
2023-01-09 0.497839 0.371737 0.366683 0.524772
2023-01-10 0.497839 0.677077 0.542580 1.384272
（2）默认索引
df = pd.DataFrame(abs(np.random.randn(10, 4)), columns=list('ABCD'))
print(df)
df.index.name='no'
#默认数字序列索引
for i in df.index :
df.loc[df.index==i,['A','C']] = i
#D列赋值空值
df['D']= np.NaN
#D列偶数行赋值
for i in df.index :
if i%2 :
df.loc[df.index==i,['D']] = i
print(df)
#用fillna函数向前填充
df1 = df['D'].fillna(method='bfill')
print(df1)
效果如下：
A B C D
0 1.355061 1.784947 0.530280 0.343836
1 0.591961 1.587958 0.700280 0.096845
2 0.945876 1.036163 0.903821 0.161356
3 1.144042 1.162818 0.148023 1.971303
4 0.424846 0.960678 0.891586 1.687668
5 0.441317 2.275049 0.168477 0.297483
6 0.791475 0.894168 1.309116 1.826531
7 0.349400 0.878078 1.748874 2.238486
8 0.501033 0.608020 0.346233 2.553355
9 0.795990 1.267664 0.565392 1.510390
A B C D
no
0 0.0 1.784947 0.0 NaN
1 1.0 1.587958 1.0 1.0
2 2.0 1.036163 2.0 NaN
3 3.0 1.162818 3.0 3.0
4 4.0 0.960678 4.0 NaN
5 5.0 2.275049 5.0 5.0
6 6.0 0.894168 6.0 NaN
7 7.0 0.878078 7.0 7.0
8 8.0 0.608020 8.0 NaN
9 9.0 1.267664 9.0 9.0
no
0 1.0
1 1.0
2 3.0
3 3.0
4 5.0
5 5.0
6 7.0
7 7.0
8 9.0
9 9.0
Name: D, dtype: float64
3.iloc数据值遍历
（1）基于日期和默认索引
基于日期和默认索引，因为只能使用位置参数，所以索引无差别。
df = pd.DataFrame(abs(np.random.randn(10, 4)), index=pd.date_range('1/1/2023', periods=10),
columns=list('ABCD'))
df.index.name='date'
#按行数循环
for i in range(df.shape[0]) :
#偶数行，2,3列是C、D列
if i%2 == 0 :
df.iloc[[i],[2,3]] = 0
#第一行的CD列赋值100
df.iloc[[0],[2,3]] = 100
#
#print(df.iloc[[0],[3]].values[0][0])
print(df)
for i in range(df.shape[0]) :
# 偶数行赋值C列向下填充
if df.iloc[[i],[2]].values[0][0] == 0.0 :
df.iloc[[i],[2]]=pre_c
pre_c=df.iloc[[i],[2]]
# 偶数行赋值D列向下填充
if df.iloc[[i],[3]].values[0][0] == 0.0 :
df.iloc[[i],[3]]=pre_d
pre_d=df.iloc[[i],[3]]
print(df)
效果如下：
A B C D
date
2023-01-01 1.040645 0.369780 100.000000 100.000000
2023-01-02 1.850851 1.422875 0.066909 1.137934
2023-01-03 0.321779 0.376273 0.000000 0.000000
2023-01-04 0.316248 1.198039 1.707555 0.539617
2023-01-05 0.350327 0.144577 0.000000 0.000000
2023-01-06 0.396593 1.054268 0.791154 0.898749
2023-01-07 0.685409 1.286553 0.000000 0.000000
2023-01-08 0.366570 0.997236 1.534733 0.689972
2023-01-09 0.417907 0.823729 0.000000 0.000000
2023-01-10 1.316604 0.867192 0.514058 0.945503
A B C D
date
2023-01-01 1.040645 0.369780 100.000000 100.000000
2023-01-02 1.850851 1.422875 0.066909 1.137934
2023-01-03 0.321779 0.376273 0.066909 1.137934
2023-01-04 0.316248 1.198039 1.707555 0.539617
2023-01-05 0.350327 0.144577 1.707555 0.539617
2023-01-06 0.396593 1.054268 0.791154 0.898749
2023-01-07 0.685409 1.286553 0.791154 0.898749
2023-01-08 0.366570 0.997236 1.534733 0.689972
2023-01-09 0.417907 0.823729 1.534733 0.689972
2023-01-10 1.316604 0.867192 0.514058 0.945503
注意：
df.iloc[[0],[3]]的数据类型
print(type(df.iloc[[0],[3]]))
print(type(df.iloc[[0],[3]].values[0]))
print(type(df.iloc[[0],[3]].values[0][0]))
分别是dataframe，数组，浮点
4.总结对比
（1）loc
- 行根据行标签，可以索引条件筛选，列根据列标签筛选
- 如果选取的是所有行或者所有列，可以用：代替
- 行标签选取的时候，两端都包含，默认索引时[0:3]指的是0，1，2，3一共4列
- 用列标签指定列，程序可读性好
（2）iloc
- iloc基于位置索引，行列都是从0开始的。
- iloc无法用索引条件筛选，如索引是日期类型，无法选择日期范围。
- 行标签选取，左包括，右不包括
- 如果dataframe列数多，对应选列值不太方便，程序可读性差
对比说明：
print(df.iloc[0:2,1:3])
print(df.loc[0:2,['C','D']])
iloc，行选择0:2，不包括第二行，1:3列，1-3对应BCD列，筛选后不包括D列
loc，行选择0:2，包括第二行。
结果：
B C
no
0 1.141945 0.0
1 1.010452 1.0
C D
no
0 0.0 NaN
1 1.0 1.0
2 2.0 NaN