内容 (Content)

1. Identifying data types识别数据类型
2. Fixing the rows and columns固定行和列
3. Imputing/removing missing values插补/删除缺失值
4. Handling outliers处理异常值
5. Standardising the values标准化值
6. Fixing invalid values修正无效值
7. Filtering the data过滤数据

1.识别数据类型 (1. Identifying Data Types)

1. Find Categorical Data查找分类数据

list(df.columns[df.dtypes == ‘object’])

列表(df.columns [df.dtypes =='object'])

But, Categorical data can exist in Numerical format. eg. , days of a month, months(1–12), waist-size (24–38).

但是，分类数据可以数字格式存在。 例如。 ，每月的某几天，几个月(1–12)，腰围(24–38)。

2. Distinguish between Numerical and Categorical Data

2.区分数值数据和分类数据

df.nunique().sort_values()

df.nunique()。sort_values()

Categorical — The count of unique values should be 30 or less.

分类-唯一值的计数应小于或等于30。

对数值数据进行运算 (Perform operations on numerical data)

Correlations — should only be done on numeric variables.

关联-仅应在数字变量上进行。

uniqueCount = df.nunique()

uniqueCount = df.nunique()

numerical_columns = list (uniqueCount [ uniqueCount > 30 ].keys())

numeric_columns =列表(uniqueCount [uniqueCount> 30] .keys())

df[numerical_columns] .corr()

df [numerical_columns] .corr()

对数值数据进行可视化 (Perform Visualisations on numerical data)

Scatter plot should always be feed with numerical data each side.

散点图应始终在两侧馈送数值数据。

2.固定行和列 (2. Fixing the rows and columns)

2.1检查格式 (2.1 Check Formatting)

1. Check the file format检查文件格式

pd.read_csv() / pd.read_tsv()

pd.read_csv()/ pd.read_tsv()

2. Check the separator type

2.检查分隔符类型

pd.read_csv(sep = ‘|’)

pd.read_csv(sep ='|')

2.2固定行 (2.2 Fixing rows)

1. Ignore irrelevant rows — Rows other than the column name headers , use skiprows = 2

   忽略不相关的行-列名标题以外的行，请使用skiprows = 2

2. Delete summary rows — We derive such insights from dataset, hence not required.删除摘要行-我们从数据集中获得此类见解，因此不是必需的。
3. Delete extra rows — column number indicator, subsection, new page.删除多余的行-列号指示符，小节，新页面。

df = df.loc[‘condition’]

df = df.loc ['condition']

2.3固定柱 (2.3 Fixing columns)

1. Add column names if missing.如果缺少，请添加列名称。

df.columns.values[i] = ‘Column_name’

df.columns.values [i] ='列名'

2. Rename columns consistently: Abbreviations, encoded columns.

2.一致地重命名列：缩写，编码列。

df.rename(columns = {‘old_name’ : ‘new_name’} )

df.rename(columns = {'old_name'：'new_name'})

3. Align misaligned columns -> Manual fix on file.

3.对齐未对齐的列->手动修复文件。

4. Delete columns having less analytical value. Like name, (system-generated)id columns.

4.删除分析值较小的列。 像名称一样，(系统生成的)id列。

df.drop(columns = [‘col1’, ‘col2’])

df.drop(columns = ['col1'，'col2'])

df.drop([‘col1’, ‘col2’], axis = 1)

df.drop(['col1'，'col2']，轴= 1)

5. Split/merge columns to get more understandable data.

5.拆分/合并列以获取更多可理解的数据。

df[‘avg’] = (df[‘M1’] + df[‘M2’]) /2 ;

df ['avg'] =(df ['M1'] + df ['M2'])/ 2;

df[‘fullname’] = df[‘firstname] + ‘ ‘ + df[lastname]

df ['fullname'] = df ['firstname] +''+ df [lastname]

3.插补/删除缺失值 (3. Imputing/removing missing values)

3.1用空值替换空值 (3.1 Replace Empty values with Nulls)

Need to replace the empty strings or object NA,XX with nan

需要用nan替换空字符串或对象NA，XX

df.replace(r’^\s*$’, np.NaN, regex=True)

df.replace(r'^ \ s * $'，np.NaN，regex = True)

df.replace(r’NA’, np.NaN, regex=True)

df.replace(r'NA'，np.NaN，regex = True)

df.replace(r’XX’, np.NaN, regex=True)

df.replace(r'XX'，np.NaN，regex = True)

3.2检查空值 (3.2 Check Nulls)

1. List null values列出空值

df.columns[df.isna().any()].tolist()

df.columns [df.isna()。any()]。tolist()

2. Check the Percentage of nulls

2.检查空值百分比

df.isnull().sum()/len(df)*100).sort_values(ascending = False)

df.isnull()。sum()/ len(df)* 100).sort_values(升序= False)

3. Profilers — Check Null value visualizations.

3.事件探查器—检查“空值”可视化。

3.3删除行/列 (3.3 Removing Rows/Columns)

1. Removing the outlier data删除异常数据

df = df[df[‘field1’] < Outliers]

df = df [df ['field1'] <离群值]

2. Removing data if the target value is missing

2.如果缺少目标值，则删除数据

df1 = df[~df[‘target-value’].isnull()].copy()

df1 = df [〜df ['target-value']。isnull()]。copy()

3. High Percentage of nulls

3.高百分比的空值

df.drop(columns = [‘col1’, ‘col2’]) / df.drop([‘col1’, ‘col2’], axis = 1)

df.drop(columns = ['col1'，'col2'])/ df.drop(['col1'，'col2']，轴= 1)

df = df.loc[:, df.isnull().mean() < .95]

df = df.loc [:, df.isnull()。mean()<.95]

3.4.1缺失值类型 (3.4.1 Missing Values Types)

MCAR: It stands for Missing Completely At Random. The reason behind a missing value is not dependent on any other feature.

MCAR ：代表随机完全丢失。 缺少值的原因不依赖于任何其他功能。

MAR: It stands for Missing At Random. The reason behind a missing value may be associated with some other features.

MAR ：代表“随机失踪”。 缺少值的原因可能与某些其他功能有关。

MNAR: It stands for Missing Not At Random. There is a specific reason behind a missing value.

MNAR ：代表不随机丢失。 缺少值背后有特定原因。

3.4.2缺失值处理 (3.4.2 Missing Values Treatment)

df.[field].fillna() → fill null values

df。[field] .fillna() →填充空值

1. Replacing all nulls with zeros →用零替换所有空值→

df.fillna(0)

df.fillna(0)

2. Imputing values using Mean, Median, Mode

2.使用均值，中位数，众数估算值

Mean — continuous, if data set does not have outliers.

均值-连续(如果数据集没有异常值)。

df[‘field1’].mean()

df ['field1']。mean()

Median — Has outliers

中位数-有异常值

df[‘field1’].median()

df ['field1']。median()

Mode — Max occurrence value, categorical.

模式-最大发生值(绝对)。

df[‘field1’].mode()[0]

df ['field1']。mode()[0]

3. Fill with a relevant value by looking at other columns of the same row.

3.通过查看同一行的其他列来填充相关值。

df[‘field’] = df.apply(lambda x : transform(x), axis =1)

df ['field'] = df.apply(lambda x：transform(x)，axis = 1)

4.处理异常值 (4. Handling outliers)

Q3 = np.percentile(df[‘field1’], 75)

Q3 = np.percentile(df ['field1']，75)

Q1 = np.percentile(df[‘field1’], 25)

Q1 = np.percentile(df ['field1']，25)

IQR = Q3 — Q1

IQR = Q3-Q1

Outliers = Q3 + 1.5 * IQR

离群值= Q3 + 1.5 * IQR

df = df[df[‘field1’] < Outliers] df[‘field1’].plot(kind=’box’)

df = df [df ['field1'] <离群值] df ['field1']。plot(kind ='box')

5.标准化值 (5. Standardising the values)

1. Standardise Precision — for better presentation of data. e.g. change 4.5312341 kg to 4.53 kg.

   标准化精度 -更好地呈现数据。 例如，将4.5312341公斤更改为4.53公斤。

df[‘field’] = df[‘field’] .apply(lambda x : round(x,2))

df ['field'] = df ['field'] .apply(lambda x：round(x，2))

2. Scale Values/Standardise Units — Ensure all observations under one variable are expressed in a common and consistent unit.

2. 标度值/标准化单位 -确保在一个变量下的所有观察值均以相同且一致的单位表示。

df[‘field’] = df[‘field’] .apply(lambda x : transform(x))

df ['field'] = df ['field'] .apply(lambda x：transform(x))

3. Standardise Format — It is important to standardise the format of other elements such as date and name. e.g., change 23/10/16 to 2016/10/23.

3. 标准化格式—标准化其他元素(例如日期和名称)的格式很重要。 例如，将23/10/16更改为2016/10/23。

df[‘field1’] = pd.to_datetime(df[‘field1’], format = ’%d%b%Y:%H:%M:%S. %f’)

df ['field1'] = pd.to_datetime(df ['field1']，format ='％d％b％Y：％H：％M：％S。％f')

strftimes — Check the date format from this link.

strftimes-从此链接检查日期格式。

4. Standardise Case — String variables may take various casing styles, e.g. FULLCAPS, lowercase, Title Case, Sentence case, etc.

4. 标准化大小写 —字符串变量可以采用各种大小写样式，例如FULLCAPS，小写字母，标题大小写，句子大小写等。

df[‘field1’] = df[‘field1’].str.title()

df ['field1'] = df ['field1']。str.title()

5. Remove Characters — Remove extra characters such as common prefixes/suffixes, leading/trailing/multiple spaces.

5. 删除字符 -删除多余的字符，例如常见的前缀/后缀，前导/后缀/多个空格。

df[‘field1’] = arrests[‘field1’].str.replace(‘$’, ‘’ )

df ['field1'] =逮捕['field1']。str.replace('$'，'')

df[‘field1’] = df[‘field1’].str.strip()

df ['field1'] = df ['field1']。str.strip()

6.修正无效值 (6. Fixing invalid values)

1. Encode unicode properly — In case the data is being read as junk characters, try to change the encoding.

   正确编码unicode-如果数据被读取为垃圾字符，请尝试更改编码。

df= pd.read_csv(‘file.csv’,encoding =’cp1252')

df = pd.read_csv('file.csv'，encoding ='cp1252')

2. Convert incorrect data types — Change the incorrect data types to the correct data types for ease of analysis.

2. 转换不正确的数据类型—将不正确的数据类型更改为正确的数据类型，以便于分析。

df[‘field1’] = df[‘field1’].astype(int)

df ['field1'] = df ['field1']。astype(int)

df[toNumFieldsArray] = df[toNumFieldsArray].apply(pd.to_numeric, errors=’coerce’,axis=1)

df [toNumFieldsArray] = df [toNumFieldsArray] .apply(pd.to_numeric，errors ='coerce'，axis = 1)

3. Correct values that lie beyond the range — If some values lie beyond the logical range.

3. 纠正超出范围的值-如果某些值超出逻辑范围。

df.[‘field1’].describe() -> min/max values

df。['field1']。describe()->最小值/最大值

df.[‘field1’].plot(kind = ‘box’)

df。['field1']。plot(kind ='box')

4. Correct values that do not belong to the list — Remove values that do not belong to a list. eg. a data set of blood groups of individuals, strings ‘E’ and ‘F’ are invalid values.

4. 纠正不属于列表的值—删除不属于列表的值。 例如。 在个人血型数据集中，字符串“ E”和“ F”是无效值。

df[‘field’].value_counts()

df ['field']。value_counts()

5. Fix incorrect structure — Values that do not follow a defined structure can be removed Eg. a phone number of 12 digits is an invalid value.

5. 修复错误的结构-可以删除不遵循定义的结构的值，例如。 12位电话号码是无效值。

df[‘len’] = df[‘filed1’].apply(lambda x : len(str(x)))

df ['len'] = df ['filed1']。apply(lambda x：len(str(x)))

6. Validate internal rules — Internal rules, if present, should be correct and consistent. eg. a product’s date of delivery cannot be less than date of purchase.

6. 验证内部规则-内部规则(如果存在)应正确且一致。 例如。 产品的交货日期不能少于购买日期。

df[df[‘field1’]>df[‘field2’]]

df [df ['field1']> df ['field2']]

7.过滤数据 (7. Filtering the data)

1. Deduplicate data — Remove identical rows and rows in which some columns are identical.

   重复数据删除—删除相同的行和某些列相同的行。

df.drop_duplicates(subset =”field1",keep = first/last, inplace = True)

df.drop_duplicates(subset =“ field1”，keep = first / last，inplace = True)

2. Filter rows — Filter rows by segment and date period to obtain only rows that are relevant to the analysis.

2. 筛选行—按细分和日期期间筛选行，以仅获取与分析相关的行。

df = df.loc[‘condition’]

df = df.loc ['condition']

3. Filter columns — Filter columns that are relevant to the analysis.

3. 过滤器列-与分析相关的过滤器列。

df_derived = df.filter(regex = “^COMMON_EXP”, axis = 1)

df_derived = df.filter(regex =“ ^ COMMON_EXP”，轴= 1)

4. Binning Data — converting a numerical data to categorical.

4. 合并数据 -将数字数据转换为分类数据。

df[‘field-group’] = pd.cut(df[‘field1’] , bins=np.linspace (min,max,bin_count))

df ['field-group'] = pd.cut(df ['field1']，bins = np.linspace(min，max，bin_count))