udemy python data analysis

1.1. Foreword This way we use the memory analysis framework pandas to analyze the daily PV.1.2. Praise to Pandas In fact, personal to pandas this module is quite favorable. I use pandas to complete many of the day-to-day practical gadgets, such as the production of Excel reports, simple data migration, and so on. To me, pandas is a memory MySQL, I usually call him the program SQL. 1.3. Pandas

)#Display OutputFigure () Gray () forIinchRange (File_len): Subplot (3,4,i + 1) pic= Immatrix[i].reshape (180,360) pic= Pic[::-1]#picshow = rot90 (pic,4)imshow (pic) Colorbar () show ()#Convert to sample populationX =Immatrix. T#get to this sizeM,n = X.shape[0:2]#obtain the average of each sampleMeanval = mean (X,axis =0)#Tempmean = Tile (Meanval, (64800,1))#Sample matrix de -CentralizedX = X-tile (Meanval, (64800,1))#Calculate covarianceS = dot (x.t,x)/(m-1)#calculate eigenvalues eg and eigenve

First, Brief introduction matplotlib1. Matplotlib is a powerful toolkit for Python drawing and data visualization2. Installation method: Pip Install Matplotlib3. Citation method: Import Matplotlib.pyplot as Plt4. Drawing function: Plt.plot ()5. Display Image: Plt.show () --Linetype LineStyle (-,-.,--,.. ) -colour color (b,g,r,y,k,w,... 2, plot function draw multiple curves 3, Pandas package

???IndexP.asfreq (' M ', ' Start ') #将年度数据转换为月度的形式, converted to the month of the yearP.asfreq (' M ', ' End ') #将年度数据转换为月度的形式, converted to December of the yearP1=PD. Period (' freq= ', ' A-jun ')P1.asfreq (' m ', ' Start ') #Period (' 2015-07 ', ' m ')P1.asfreq (' m ', ' End ') #Period (' 2016-06 ', ' m ')P2=PD. Period (' 2016-09 ', ' M ')P2.asfreq (' A-jun ') #2016年9月进行频率转换, equivalent to 2017 years in the time frequency ending in JuneRng=pd.period_range (' 2006 ', ' freq= ', ' A-dec ')Ts=ser

‘) #颜色深蓝cup_style = bra.groupby(‘cup‘)[‘cup‘].count() #cup列唯一值得数量cup_styleplt.figure(figsize=(8,6),dpi=80)labels = list(cup_style.index)plt.xlabel(‘cup‘) #x轴为cupplt.ylabel(‘count‘) #y轴为count数量plt.bar(range(len(labels)),cup_style,color=‘royalblue‘,alpha=0.7) #alpha为透明度plt.xticks(range(len(labels)),labels,fontsize=12)plt.grid(color=‘#95a5a6‘,linestyle=‘--‘,linewidth=1,axis=‘y‘,alpha=0.6)plt.legend([‘user-count‘])for x,y in zip(range(len(labels)),cup_style):plt.text(x,y,y,ha=‘center‘,va=‘bottom‘)co

="bfill"))‘‘‘------Back fill------One, threea-0.211055-2.869212 0.022179b-0.870090-0.878423 1.071588c-0.870090-0.878423 1.071588d-0.203259 0.315897 0.495306e-0.203259 0.315897 0.495306f 0.490568-0.968058-0.999899g 1.437819-0.370934-0.482307H 1.437819-0.370934- 0.482307 ‘‘‘Print ('------Average fill------') Print (Df.fillna (Df.mean ()))‘‘‘------Average fill------One, threea-0.211055-2.869212 0.022179b 0.128797-0.954146 0.021373c-0.870090-0.878423 1.071588d 0.128797-0.95

Use Python for data analysis _ Pandas _ basic _ 2, _ pandas_2Reindex method of Series reindex In [15]: obj = Series([3,2,5,7,6,9,0,1,4,8],index=['a','b','c','d','e','f','g', ...: 'h','i','j'])In [16]: obj1 = obj.reindex(['a','b','c','d','e','f','g','h','i','j','k'])In [17]: obj1Out[17]:a 3.0b 2.0c 5.0d 7.0e 6.0f 9.0g 0.0h 1.0i 4.0j

), 'STD': List (Np.diag (np.sqrt (Res.cov_params ))),'T': List (res.tvalues),'Sig': [I forIinchMap (lambda x:float(x), ("". Join ("{:. 4f},"*len (res.pvalues)). Format (*list (res.pvalues)). Rstrip (","). Split (",")]}returnvalue= {'Model': Model,'coefficient': Coefficient}print (returnvalue){ 'Model': { 'DF':3.0, 'N':665, 'prob_f_statistic':1.185607423551511E-17, 'R_squared_adj':0.11247707470462853, 'f_statistic':29.049896130