<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Pandas DataFrame
Step1:  V4 grade (범주형 데이터형)
LC assigned loan grade
A,B,C,D,E,F,G = {1, 2, 3, 4, 5, 6, 7}
Step2:  V5 sub_grade (범주형 데이터형)
LC assigned loan subgrade
1, 2, 3, 4, 5
Step3:  V6 emp_title (범주형 데이터형)
The job title supplied by the Borrower when applying for the loan.*
True = 1, False = 0
Step4:  V7 emp_length (범주형 데이터형)
Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. 
< 1' = 0, 10+ = 10, 'n/a' = 11
Step5:  V8 home_ownership (범주형 데이터형)
The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are
Step6:  V10 verification_status (범주형 데이터형)
Indicates if income was verified by LC, not verified, or if the income source was verified
Source Verified, Verified = 1, Not Verified = 0
Step7:  V11 issue_d (범주형 데이터형)
The month which the loan was funded
mm 으로 변환
Step8:  V14 purpose (범주형 데이터형)
A category provided by the borrower for the loan request. 
15개 범주 = {1
Step9:  V23 initial_list_status (범주형 데이터형)
The initial listing status of the loan. Possible values are – W, F
W = 1, F = 0
<ASSISTANT_TASK:>
Python Code:
lc_data = pd.DataFrame.from_csv('./lc_dataframe(cleaning).csv')
lc_data = lc_data.reset_index()
lc_data.tail()
Explanation: Pandas DataFrame
End of explanation
x = lc_data['grade']
sns.distplot(x, color = 'r')
plt.show()
Explanation: V4 grade (범주형 데이터형)
LC assigned loan grade
A,B,C,D,E,F,G = {1, 2, 3, 4, 5, 6, 7}
End of explanation
x = lc_data['sub_grade']
sns.distplot(x, color = 'g')
plt.show()
Explanation: V5 sub_grade (범주형 데이터형)
LC assigned loan subgrade
1, 2, 3, 4, 5
End of explanation
x = lc_data['emp_title']
plt.hist(x)
plt.show()
Explanation: V6 emp_title (범주형 데이터형)
The job title supplied by the Borrower when applying for the loan.*
True = 1, False = 0
End of explanation
x = lc_data['emp_length']
sns.distplot(x, color = 'r')
plt.show()
Explanation: V7 emp_length (범주형 데이터형)
Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. 
< 1' = 0, 10+ = 10, 'n/a' = 11
End of explanation
x = lc_data['home_ownership']
sns.distplot(x, color = 'g')
plt.show()
Explanation: V8 home_ownership (범주형 데이터형)
The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER
Mortgage, None, Other, Own, Rent = {1, 2, 3, 4, 5}
End of explanation
x = lc_data['verification_status']
sns.distplot(x)
plt.show()
Explanation: V10 verification_status (범주형 데이터형)
Indicates if income was verified by LC, not verified, or if the income source was verified
Source Verified, Verified = 1, Not Verified = 0
End of explanation
x = lc_data['issue_d']
sns.distplot(x, color = 'r')
plt.show()
Explanation: V11 issue_d (범주형 데이터형)
The month which the loan was funded
mm 으로 변환
End of explanation
x = lc_data['purpose']
sns.distplot(x, color = 'g')
plt.show()
Explanation: V14 purpose (범주형 데이터형)
A category provided by the borrower for the loan request. 
15개 범주 = {1:15} (1부터 15까지)
End of explanation
x = lc_data['initial_list_status']
plt.hist(x)
plt.show()
Explanation: V23 initial_list_status (범주형 데이터형)
The initial listing status of the loan. Possible values are – W, F
W = 1, F = 0
End of explanation
<END_TASK>