Wendi's Learning

import pandas as pd import numpy as np import matplotlib.pyplot as plt import chart_studio.plotly as py %matplotlib inline Plotly, Cufflinks from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot import cufflinks as cf # For Notebooks init_notebook_mode(connected=True) # For offline use cf.go_offline() df = pd.DataFrame(np.random.randn(100,4), columns = 'A B C D'.split()) ..

f = plt.figure() # select only 0~29 index df3.iloc[0:30].plot.area(alpha = 0.4, ax = f.gca()) # move legend outside of the plot plt.legend(loc = 'center left', bbox_to_anchor = (1.0, 0.5)) plt.show()​ import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt %matplotlin inline df1 = pd.read_csv('df1', index_col=0) df2 = pd.read_csv('df2') df1.head() df2.head() ..

import seaborn as sns %matplotlib inline Load Data tips = sns.load_dataset('tips') tips.head() Distribution Plot # distribution plot sns.displot(tips['total_bill # kde sns.distplot(tips['total_bill'], kde = True, bins = 30) Joint Plot # kind = plot 종류 sns.jointplot(x = 'total_bill', y = 'tip', data = tips, kind = 'scatter') sns.jointplot(x = 'total_bill', y = 'tip', data = tips, kind = 'hex') sn..

import matplotlib.pyplot as plt %matplotlib inline Basic Plot import numpy as np x = np.linspace(0,5,11) y = x ** 2 plt.plot(x, y, 'red') plt.xlabel('X Axis') plt.ylabel('Y Axis') plt.title('Title') plt.show() Subplot plt.subplot(1,2,1) # nrow, ncol, index(1~) plt.plot(x, y, 'r') plt.subplot(1,2,2) plt.plot(y, x, 'b') Object Oreinted Method fig = plt.figure() axes = fig.add_axes([0.1, 0.1, 0.8, ..

Pandas import numpy as np import pandas as pd labels = ['a','b','c'] my_list = [10,20,30] arr = np.array([10,20,30]) my_dict = {'a' : 10, 'b' : 20, 'c' : 30} Series pd.Series(data = my_list) pd.Series(data = my_list, index = labels) pd.Series(arr) pd.Series(arr,labels) pd.Series(my_dict) ser_1 = pd.Series([1,2,3,4], index = ['A','B','C','D']) ser_1 ser_2 = pd.Series([4,3,2,1],['B','C','D','F']) ..

Array import numpy as np my_list = [1,2,3] np.array(my_list) my_matrix = [[1,2,3],[4,5,6,],[7,8,9,]] np.array(my_matrix) Arange np.arange(11) np.arange(1,11) np.arange(1,11,2) # interval Zeros, Linspace np.zeros(3) np.zeros((5,5)) # matrix = tuple np.linspace(0,10,3) # linearly spaced in area np.linspace(0,10,50) Random np.random.rand(3) # [0,1) np.random.randn(2,5) # standard normal np.random.r..