Kin Lim Lee 分析了8个简单的预处理代码，一共涵盖8个场景，分别是：
删除多列、更改数据类型、将分类变量转换为数字变量、检查缺失数据、删除列中的字符串、删除列中的空格、用字符串连接两列（带条件）、转换时间戳（从字符串到日期时间格式）
删除多列
Delete multiple columns
Not all columns are useful for data analysis. Df.drop makes it easy to delete the columns you specify.
在进行数据分析时，并非所有的列都有用，用df.drop可以方便地删除你指定的列。
def drop_multiple_col(col_names_list, df):
AIM -> Drop multiple columns based on their column names
INPUT -> List of column names, df
OUTPUT -> updated df with dropped columns
------
df.drop(col_names_list, axis=1, inplace=True)
return df
转换数据类型
Convert data type
When the data set gets bigger, you need to convert the data type to save memory.
当数据集变大时，需要转换数据类型来节省内存。
def change_dtypes(col_int, col_float, df):
AIM -> Changing dtypes to save memory
INPUT -> List of column names (int, float), df
OUTPUT -> updated df with smaller memory
------
df[col_int] = df[col_int].astype( int32 )
df[col_float] = df[col_float].astype( float32 )
将分类变量转换为数值变量
Convert categorical variables to numeric variables
Some machine learning models require variables to be in numeric format. This requires first converting the categorical variable to a numeric variable. At the same time, you can also keep categorical variables for data visualization.
一些机器学习模型要求变量采用数值格式。这需要先将分类变量转换为数值变量。同时，你也可以保留分类变量，以便进行数据可视化。
def convert_cat2num(df):
# Convert categorical variable to numerical variable
num_encode = { col_1 : { YES :1, NO :0},
col_2 : { WON :1, LOSE :0, DRAW :0}}
df.replace(num_encode, inplace=True)
检查缺失数据
Check for missing data
If you want to check the amount of missing data per column, using the following code is the fastest way. It allows you to better understand which columns are missing more data and determine how to proceed with the next step of data cleansing and analysis.
如果你要检查每列缺失数据的数量，使用下列代码是最快的方法。可以让你更好地了解哪些列缺失的数据更多，从而确定怎么进行下一步的数据清洗和分析操作。
def check_missing_data(df):
# check for any missing data in the df (display in descending order)
return df.isnull().sum().sort_values(ascending=False)
删除列中的字符串
Delete strings in columns
Sometimes new characters or other strange symbols appear in the string column, which can be handled simply by using df[‘col_1’].replace Drop it.
有时候，会有新的字符或者其他奇怪的符号出现在字符串列中，这可以使用df[‘col_1’].replace很简单地把它们处理掉。
def remove_col_str(df):
# remove a portion of string in a dataframe column - col_1
df[ col_1 ].replace(, , regex=True, inplace=True)
# remove all the characters after &# (including &#) for column - col_1
df[ col_1 ].replace( &#.* , , regex=True, inplace=True)
删除列中的空格
Delete spaces in columns
When data is confusing, anything can happen. There are often some spaces at the beginning of the string. The following code is very useful when deleting spaces at the beginning of a string in a column.
数据混乱的时候，什么情况都有可能发生。字符串开头经常会有一些空格。在删除列中字符串开头的空格时，下面的代码非常有用。
def remove_col_white_space(df):
# remove white space at the beginning of string
df[col] = df[col].str.lstrip()
用字符串连接两列（带条件）
Connect two columns with strings (with condition)
This code is helpful when you want to conditionally join two columns together with a string. For example, you can set some letters at the end of the first column and then use them to connect to the second column.
As needed, the letters at the end can also be deleted after the connection is complete.
当你想要有条件地用字符串将两列连接在一起时，这段代码很有帮助。比如，你可以在第一列结尾处设定某些字母，然后用它们与第二列连接在一起。根据需要，结尾处的字母也可以在连接完成后删除。
def concat_col_str_condition(df):
# concat 2 columns with strings if the last 3 letters of the first column are pil
mask = df[ col_1 ].str.endswith( pil , na=False)
col_new = df[mask][ col_1 ] + df[mask][ col_2 ]
col_new.replace( pil , , regex=True, inplace=True) # replace the pil with emtpy space
转换时间戳（从字符串到日期时间格式）
Conversion timestamp (from string to datetime format)
When processing time series data, we are likely to encounter timestamp columns in string format.
This means converting the string format to a datetime format (or other format specified according to our needs) for meaningful analysis of the data.
在处理时间序列数据时，我们很可能会遇到字符串格式的时间戳列。这意味着要将字符串格式转换为日期时间格式(或者其他根据我们的需求指定的格式) ，以便对数据进行有意义的分析。
def convert_str_datetime(df):
AIM -> Convert datetime(String) to datetime(format we want)
INPUT -> df
OUTPUT -> updated df with new datetime format
------
df.insert(loc=2, column= timestamp , value=pd.to_datetime(df.transdate, format= %Y-%m-%
一般在DataFrame中，简易获取统计信息的函数：
df.count() #非空元素计算
df.min() #最小值
df.max() #最大值
df.idxmin() #最小值的位置，类似于R中的which.min函数
df.idxmax() #最大值的位置，类似于R中的which.max函数
df.quantile(0.1) #10%分位数
df.sum() #求和
df.mean() #均值
df.median() #中位数
df.mode() #众数
df.var() #方差
df.std() #标准差
df.mad() #平均绝对偏差
df.skew() #偏度
df.kurt() #峰度
df.describe() #一次性输出多个描述性统计指标
