31. 加载电信客户流失数据集

df = pd.read_csv("Telco-Customer-Churn.csv")

print(df.head(5))

32. 统计每一列数据的缺失值

print(df.isnull())
print(df.isnull().sum())

33. 正确设置数据列的类型

print(df.info())
print(df["TotalCharges"].value_counts())

median = df["TotalCharges"][df["TotalCharges"] != " "].median()
df.loc[df["TotalCharges"] == " ", "TotalCharges"] = median

df["TotalCharges"] = df["TotalCharges"].astype(float)
print()
print(df["TotalCharges"].value_counts())

34. 将类别字段转换成cat类型

print(df.columns)
number_columns = ["tenure", "MonthlyCharges", "TotalCharges"]
for column in number_columns:
    df[column] = df[column].astype(float)
for column in set(df.columns) - set(number_columns):
    df[column] = pd.Categorical(df[column])
print(df.info())

35. 对cat类型字段数据统计

print(df.describe(include=["category"]))

36. churn字段的数据分布

print(df["Churn"].value_counts())

37. 多维度查看MonthlyCharges字段统计

print(df.columns)
print(df.groupby(["Churn", "PaymentMethod"])["MonthlyCharges"].mean())

38. Churn字段的数据映射

print(df["Churn"].value_counts())
df["Churn"] = df["Churn"].map({"Yes": 1, "No": 0})
print()
print(df["Churn"].value_counts())

39. 查看字段相关矩阵

print(df.head(3))
print(df.info())

print()
print(df.corr())

40. 从数据集中采样数据行

print(df.sample(10))

课程参考链接：https://ke.qq.com/course/4000626#term_id=104152097