Pandas read and modify excel operation strategy in Python (code example)

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Environment: python 3.6.8

Let’s take a certain Miser number as an example:

>>> pd.read_excel('1.xlsx', sheet_name='Sheet2')
     名字   等级 属性1   属性2  天赋
0  四九幻曦  100  自然  None  21
1  圣甲狂战  100  战斗  None   0
2  时空界皇  100   光    次元  27

We use the pd.read_excel() function here to read excel. Let’s take a look at the API of read_excel(). Here we only intercept Some commonly used parameters:

pd.read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None)

io: Obviously, it is the path name string of the excel file

(if there is Chinese, it is python2 Laotie needs to use decode() to decode into unicode string)
For example:

>>> pd.read_excel('例子'.decode('utf-8))

sheet_name: Return the specified sheet
If sheet_name is specified as None, the entire sheet will be returned
If multiple sheets need to be returned, sheet_name## can be returned #Specify as a list, for example ['sheet1', 'sheet2']You can specify the selected

sheet based on the name string or index of sheet

>>> # 如:
>>> pd.read_excel('1.xlsx', sheet_name=0)
>>> pd.read_excel('1.xlsx', sheet_name='Sheet1')
>>> # 返回的是相同的 DataFrame

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>>> # 如:
>>> pd.read_excel('1.xlsx', sheet_name=1, usecols=['等级', '属性1'])
>>> pd.read_excel('1.xlsx', sheet_name=1, usecols=[1,2])
>>> # 返回的是相同的 DataFrame

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.iloc or .loc objects

>>> # 读取文件
>>> data = pd.read_excel("1.xlsx", sheet_name="Sheet1")

>>> # 找到 等级 这一列，再在这一列中进行比较
>>> data['等级'][data['名字'] == '泰格尔'] += 1
>>> print(data)

>>> data
     名字   等级 属性1   属性2  天赋
0  艾欧里娅  100  自然     冰  29
1   泰格尔   81   电    战斗  16
2  布鲁克克  100   水  None  28

data.to_excel('1.xlsx', sheet_name='Sheet1', index=False, header=True)

True, whether to add row index, just go to the picture above!

False, right is True

True, whether Add a column mark and picture it!
The left one is False, the right one is True

io, sheet_name parameter usage is the same as function pd. read_excel()

data['column name'] = [value 1, value 2, ...]

>>> data['特性'] = ['瞬杀', 'None', '炎火']
>>> data
     名字   等级 属性1   属性2  天赋    特性
0  艾欧里娅  100  自然     冰  29    瞬杀
1   泰格尔   80   电    战斗  16  None
2  布鲁克克  100   水  None  28    炎火

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data.loc[num of the row] = [value 1, value 2, ...], (note the difference with
.iloc difference)

>>> data.loc[3] = ['小火猴', 1, '火', 'None', 31, 'None']
>>> data
     名字   等级 属性1   属性2  天赋    特性
0  艾欧里娅  100  自然     冰  29    瞬杀
1   泰格尔   80   电    战斗  16  None
2  布鲁克克  100   水  None  28    炎火
3   小火猴    1   火  None  31  None

.drop()function

>>> # 删除列, 需要指定axis为1，当删除行时，axis为0
>>> data = data.drop('属性1', axis=1) # 删除`属性1`列
>>> data
     名字   等级   属性2  天赋    特性
0  艾欧里娅  100     冰  29    瞬杀
1   泰格尔   80    战斗  16  None
2  布鲁克克  100  None  28    炎火
3   小火猴    1  None  31  None

>>> # 删除第3,4行，这里下表以0开始，并且标题行不算在类, axis用法同上
>>> data = data.drop([2, 3], axis=0)
>>> data
     名字   等级 属性2  天赋    特性
0  艾欧里娅  100   冰  29    瞬杀
1   泰格尔   80  战斗  16  None

>>> # 保存
>>> data.to_excel('2.xlsx', sheet_name='Sheet1', index=False, header=True)

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