代写python pandas 国外、代做pandas python程序

日期：2018-05-26 06:22

import pandas as pd

import numpy as np

import statsmodels as sm

import sklearn as skl

import sklearn.preprocessing as preprocessing

import sklearn.linear_model as linear_model

import sklearn.cross_validation as cross_validation

import sklearn.metrics as metrics

import seaborn as sns

import math

import matplotlib.pyplot as plt

original_data=pd.read_csv("C:\\Users\\ccy\\Desktop\\Adult dataset analysis\\adult.data.txt",names=['Age','Workclass','fnlwgt','Education','Education_Num','Matial Status','Ocaaupation','Relationship','Race','Sex','Capital Gain','Cpital Loss','Hours per week','Country','Target'],sep=r'\s*,\s*',engine='python')

original_data.dropna(how="all",inplace=True)

original_data.tail()

fig=plt.figure(figsize=(20,15))

cols=5

rows=math.ceil(float(original_data.shape[1]/cols))

for i,column in enumerate(original_data.columns):

ax=fig.add_subplot(rows,cols,i+1)

ax.set_title(column)

if original_data.dtypes[column]==np.object:

original_data[column].value_counts().plot(kind="bar",axes=ax)

else:

original_data[column].hist(axes=ax)

plt.xticks(rotation="vertical")

plt.subplots_adjust(hspace=0.9, wspace=0.2)

plt.savefig("C:\\Users\\ccy\Desktop\\Adult dataset analysis\\a1.png")

plt.show()

(original_data["Country"].value_counts()/original_data.shape[0]).head()

def number_encode_features(df):

result=df.copy()

encoders={}

for column in result.columns:

if result.dtypes[column]==np.object:

encoders[column]=preprocessing.LabelEncoder()

result[column]=encoders[column].fit_transform(result[column])

return result,encoders

encoded_data,_=number_encode_features(original_data)

sns.heatmap(encoded_data.corr(),square=True)

plt.savefig("C:\\Users\\ccy\Desktop\\Adult dataset analysis\\a2.png")

plt.show()

original_data[["Education","Education_Num"]].head(15)

original_data[["Sex","Relationship"]].head(15)

encoded_data,encoders=number_encode_features(original_data)

for i,column in enumerate(encoded_data.columns):

ax=fig.add_subplot(rows,cols,i+1)

ax.set_title(column)

encoded_data[column].hist(axes=ax)

plt.xticks(rotation="vertical")

plt.savefig("C:\\Users\\ccy\Desktop\\Adult dataset analysis\\a3.png")

plt.show()

X_train,X_test,y_train,y_test=cross_validation.train_test_split(encoded_data.drop('Target',1),encoded_data["Target"],train_size=0.7)

scaler=preprocessing.StandardScaler()

X_train=pd.DataFrame(scaler.fit_transform(X_train.astype("float64")),columns=X_train.columns)

X_test=scaler.transform(X_test.astype("float64"))

cls=linear_model.LogisticRegression()

cls.fit(X_train,y_train)

y_pred=cls.predict(X_test)

cm=metrics.confusion_matrix(y_test,y_pred)

plt.figure(figsize=(12,12))

plt.subplot(2,1,1)

sns.heatmap(cm,annot=True,fmt="d",xticklabels=encoders["Target"].classes_,yticklabels=encoders["Target"].classes_)

plt.ylabel("Real value")

plt.xlabel("Predicted value")

print ("F1 score:%f" % skl.metrics.f1_score(y_test,y_pred))

coefs = pd.Series(cls.coef_[0], index=X_train.columns)

coefs.vaules.sort()

plt.subplot(2,1,2)

coefs.plot(kind="bar")

plt.savefig("C:\\Users\\ccy\Desktop\\Adult dataset analysis\\a4.png")

plt.show()

binary_data = pd.get_dummies(original_data)

binary_data["Target"] = binary_data["Target_>50K"]

del binary_data["Target_<=50K"]

del binary_data["Target_>50K"]

plt.subplots(figsize=(20,20))

sns.heatmap(binary_data.corr(), square=True)

plt.savefig("C:\\Users\\ccy\Desktop\\Adult dataset analysis\\a5.png")

plt.show()

X_train,X_test,y_train,y_test=cross_validation.train_test_split(binary_data.drop('Target',1),binary_data["Target"],train_size=0.7)

scaler = preprocessing.StandardScaler()

X_train = pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns)

X_test = scaler.transform(X_test)

cls = linear_model.LogisticRegression()

cls.fit(X_train, y_train)

y_pred = cls.predict(X_test)

cm = metrics.confusion_matrix(y_test, y_pred)

plt.figure(figsize=(20,20))

plt.subplot(2,1,1)

sns.heatmap(cm, annot=True, fmt="d", xticklabels=encoders["Target"].classes_, yticklabels=encoders["Target"].classes_)

plt.ylabel("Real value")

plt.xlabel("Predicted value")

print ("F1 score: %f" % skl.metrics.f1_score(y_test, y_pred))

coefs = pd.Series(cls.coef_[0], index=X_train.columns)

coefs.values.sort()

ax = plt.subplot(2,1,2)

coefs.plot(kind="bar")

plt.savefig("C:\\Users\\ccy\Desktop\\Adult dataset analysis\\a6.png")

plt.show()

print(cls.coef_)