• Python Data Anlysis Notebook
  • SublimeText File
    • Data Frame
    • Iterators
    • Importing Data in python
  • Clean Data
    • 1. Sublime笔记整理
    • 2. 函数
  • Pandas Foundations
    • 1. Inspectiong Data
    • 2. Numpy and Pandas Together
    • 3. Zip list to build a df
    • 4. Reading & Saving
    • 5. Plot
    • 6. Statistical exploratory data analysis
    • 7. Time Series
    • 8. 防盗
  • Seaborn 参考
  • Practices

Python Data Anlysis Notebook

SublimeText File

Data Frame

• read file

  pd.read_csv(path,index_col=0,chunksize=1000)
  np.shape()#读取矩阵维度
  pd.DataFrame()
  df[['var','var']] #-->dataframe
  df[1:4]
  df.head() #无数据默认前五条
  #how to refer col and row
  df.loc[label]
  df.loc[[row],[col]] #-->dataframe
  df.iloc[index]
  

• iterating over data

  result=[]
  total=0
  for chunck in pd.read_csv(path,chunksize=1000):
      result.append(sum(chunck['xcolumn']))
      #另一种方法
      total+=sum(chunck['x'])
  total=sum(result)
  

• NumPy

  np.logical_and(x,y)
  np.logical_or()
  np.logical_not()
  

• Loop

  enumerate(iterable) #--> index of the value
  for index,value in enumerate(iterable,start=10):
      print(index,value)
  loop over a dictionary:
  dic={}
  for key,value in dic.items():
      print(key,value)
  import numpy as np
  array1=np.array()
  array2=np.array()
  array_total=np.array([array1,array2])
  for val in np.nditer(array_total):
  print(val)
  
  import pandas as pd
  df=pd.read.csv()
  for label,row in df.iterrows():
      print(label)
      print (row)
  
  add a new column in to DataFrame
  for lab,row in df.iterrows():
      df.loc[lab,'name_length']=len(df['country'])
  print(df)
  #更简单的方法是使用apply function
  df['name_length']=df['country'].apply(len)
  apply(str.upper)
  
  pseudo-random
  import numpy as np
  np.random.seed(123)
  coin=np.random.randint(0,2)
  

  '''
  函数将一个数据集合（链表，元组等）中的所有数据进行下列操作：
  用传给 reduce 中的函数 function（有两个参数）先对集合中的第 1、2 个元素进行操作，
  得到的结果再与第三个数据用 function 函数运算，最后得到一个结果。
  '''
  from functools import reduce
  reduce(function,iterable)
  '''
  map() 会根据提供的函数对指定序列做映射。
  第一个参数 function 以参数序列中的每一个元素调用 function 函数，
  返回包含每次 function 函数返回值的新列表。
  '''
  map(function,iterable,...)
  '''
  filter() 函数用于过滤序列，过滤掉不符合条件的元素，返回由符合条件元素组成的新列表。
  该接收两个参数，第一个为函数，第二个为序列，
  序列的每个元素作为参数传递给函数进行判，然后返回 True 或 False，最后将返回 True 的元素放到新列表中。
  '''
  filter(function,iterable)
  

  def func():
  try:
      return function
  except [TypeError]:
      return print()
  
  # 抛出异常
  raise ValueError
  

Iterators

 #iterating over iterables
 word='DA'
 it=iter(word)
 next(it)
 print(*it) #->once per time

 zip() #-> return an iterator of tuples
 list(zip(lst1,lst2))
 print(*zipvarable)

• List comprehensions

  lst=[1,2,3,4]
  lst_add_1=[num+1 for num in lst]
  lst=[num for num in range(11)]
  
  conditionals in comprehensions
  [num**2 for num in range(10) if num%2==0]
  
  dictionary comprehensions
  {num:-num for num in range(10)}
  
  generator->analogous generator object||iterator
  (num**2 for num in range(10) if num%2==0)
  
  generator function->yield
  def num_sequence(n):
      i=0
      while i<n:
          yield i
          entryi+=1
  

Importing Data in python

 file_name=open('file.txt',mode='r') #only to read
 #'w' is to write
 text=file_name.read()
 file_name.close()
 with open('datacamp.csv','r') as datacamp #context manager
 file_objeect.readline()
 ! ls #will display the contents of your current directory.

• flat files (contrain record,row of fields or attributes, column is feature or attributes)

  #can have a header
  #numpy:storing numerical data
  import numpy as np
  filename='MNIST.txt'
  data=np.loadtxt(filename,delimiter=',',skiprows=1,usecols=[0,2],dtype=str) #跳过第一行 #只要第一和三列数据
  
  #import with different type of data
  np.genfromtext(filename,delimiter=',',names=True,dtype=None)
  
  import pandas as pd
  filename='winequality-red.csv'
  data=pd.read_csv(filename,nrows=5,header=None)#first 5 rows of the file,there is no header in this file
  
  #convert dataframe to a numpy array
  data_array=data.values
  

• Other files

  data=pd.ExcelFile(filename)
  print(data.sheet_names)#figure sheet name out
  df1=data.parse('sheetname')#sheet name
  df2=data.parse(0,skiprows=[0],parse_cols[0],names=['Country'])#sheet index
  
  #sas file
  import pandas as pd
  from sas7bdat import SAS7DBAT
  with sas7bdat('file.sas7bdat') as file:
      df_sas=file.to_data_drame()
  
  #stata file_name
  import pandas as pd
  data=pd.read_stata('file.dta')
  
  #HDF5 file
  import h5py
  file_name='file.hdf5'
  data=h5py.File(file_name,'r')
  for key in data.keys():
      print(key)
  
  #matlab file
  import scipy.io
  scipy.io.loadmat() #- read.mat files
  scipy.io.savemet() #- write.mat files
  

• Relational Database

  # Open engine in context manager
  # Perform query and save results to DataFrame: df
  
  engine = create_engine('sqlite:///Chinook.sqlite')
  with engine.connect() as con:
      rs = con.execute("SELECT LastName, Title FROM Employee")
      df = pd.DataFrame(rs.fetchmany(size=3))
      df.columns = rs.keys()
  # Print the length of the DataFrame df
  print(len(df))
  # Print the head of the DataFrame df
  print(df.head())
  
  # Import packages
  from sqlalchemy import create_engine
  import pandas as pd
  # Create engine: engine
  engine = create_engine('sqlite:///Chinook.sqlite')
  # Execute query and store records in DataFrame: df
  df = pd.read_sql_query("SELECT * From Album", engine)
  # Print head of DataFrame
  print(df.head())
  

enumerate #返回数据和其位置

 test=[1,2,3,4,5]
 for i, num in enumerate(test):
 print (i,m)

Clean Data

1. Sublime笔记整理

    # Print the head of df
    print(df.head())
    # Print the tail of df
    print(df.tail())
    # Print the shape of df
    print(df.shape)
    # Print the columns of df
    print(df.columns)
    # Rename the columns
    gapminder_melt.columns = ['country', 'year', 'life_expectancy']
    # Print the info of df,provides important information about a DataFrame
    print(df.info())
    # Print the value_counts for 'State'
    print(df.State.value_counts(dropna=False))
    # Print the value counts for 'Site Fill'
    print(df['Site Fill'].value_counts(dropna=False))

• Transpose

  # Melt airquality: airquality_melt，The id_vars represent the columns of the data you do not want to melt (i.e., keep it in its current shape),
  # while the value_vars represent the columns you do wish to melt into rows.
  airquality_melt = pd.melt(frame=df,id_vars=['Month','Day'], value_vars=['Ozone','Solar.R','Wind','Temp']，var_name='measurement', value_name='reading')
  

• Pivot_Table

  #（非重复数据）
  # Pivot airquality_melt: airquality_pivot
  airquality_pivot = airquality_melt.pivot_table(index=['Month','Day'], columns='measurement', values='reading')
  #（重复数据）
  # Pivot table the airquality_dup: airquality_pivot
  airquality_pivot = airquality_dup.pivot_table(index=['Month', 'Day'], columns='measurement', values='reading', aggfunc=np.mean)
  

  层次化索引（PIVOT之后）

  # Print the index of airquality_pivot
  print(airquality_pivot.index)
  # Reset the index of airquality_pivot: airquality_pivot_reset
  airquality_pivot_reset = airquality_pivot.reset_index()
  

• Melting and Parsing

  # Melt tb: tb_melt
  tb_melt = pd.melt(frame=tb,id_vars=['country', 'year'])
  # Create the 'gender' column
  tb_melt['gender'] = tb_melt.variable.str[0]
  # Melt ebola: ebola_melt
  ebola_melt = pd.melt(ebola, id_vars=['Date', 'Day'], var_name='type_country', value_name='counts')
  # Create the 'str_split' column
  ebola_melt['str_split'] = ebola_melt.type_country.str.split('_')
  # Create the 'type' column
  ebola_melt['type'] = ebola_melt['str_split'].str.get(0)
  # Create the 'country' column
  ebola_melt['country'] = ebola_melt['str_split'].str.get(1)
  

• Concatenation Data

  # Concatenate uber1, uber2, and uber3: row_concat
  row_concat = pd.concat([uber1,uber2,uber3])
  # Concatenate ebola_melt and status_country column-wise: ebola_tidy
  ebola_tidy = pd.concat([ebola_melt,status_country],axis=1)
  
  #Tons of files to concat
  # Import necessary modules
  import glob
  import pandas as pd
  # Write the pattern: pattern
  pattern = '*.csv'
  # Save all file matches: csv_files
  csv_files = glob.glob(pattern)
  # Print the file names
  print(csv_files)
  # Load the second file into a DataFrame: csv2
  csv2 = pd.read_csv(csv_files[1])
  # Print the head of csv2
  print(csv2.head())
  

2. 函数

df.sample() 用法参考

 df.sample(frac=0.5,replace=True,random_state=123) #按比例抽取，是否有序放回,设置随机种子

df.isnull()

 df.isnull().values.any() #.values返回array，.any()返回True False
 df.isnull().sum() #sum null

df.groupby()

    #groupby后面聚类算法__猜测只能为num__
    df.groupby('col_name').sum()

Pandas Foundations

1. Inspectiong Data

  import pandas as pd
  type(df)
  type(df.columns)
  type(df.index)
  df.describe()
  df.shape
  df.columns
  df.index
  df.iloc[:5,:]
    df.corr() #only numbers
  # broadcasting, assigning scala value to col slice broadcasts value to each rows
  df.iloc[::3,-1]=np.nan
  #Series
  low=df['Low']
  type(low)
  low.head()
  lows=low.values
  type(lows)
  #View the first few and last few rows of a DataFrame
  df.head()
  df.tail()

count values

 df['col_name'].value_counts()
 df.col_name.value_counts()

show unique value

 df['col_name'].unique()
 df.col_name.unique()

2. Numpy and Pandas Together

  import numpy as np
  #.values to represent a DataFrame df as a NumPy array.
  np_vals = df.values
  # np.log10() method to compute the base 10 logarithm
  np_vals_log10 = np.log10(np_vals)
  df_log10 = np.log10(df)
  [print(x, 'has type', type(eval(x))) for x in ['np_vals', 'np_vals_log10', 'df', 'df_log10']]

3. Zip list to build a df

  #Zip the 2 lists together into one list of (key,value) tuples: zipped
  zipped = list(zip(list_keys,list_values))
  print(zipped)
  data = dict(zipped)
  df = pd.DataFrame(data)
  print(df)

• Labeling Data

    list_labels = ['year', 'artist', 'song', 'chart weeks']
    # Assign the list of labels to the columns attribute: df.columns
    df.columns=list_labels
  

4. Reading & Saving

• Reading a flat file

    df=pd.read_csv(file_name,header=None,names=col_name,na_values={'column_name':[' -1']},parse_date=[[0,1,2]])
    df.index=df['date_time']
    df.index.name='key_date'
    #trimming redundant columns
    cols=['sunspots','definite']
    df=df[cols]
  

    # header=0 to rename the column labels
    new_labels = ['year','population']
    df2 = pd.read_csv(data_file, header=0, names=new_label
  

• Delimiters, headers, and extensions

    df2 = pd.read_csv(file_messy, delimiter=' ', header=3, comment='#')
    # Save the cleaned up DataFrame to a CSV file without the index
    df2.to_csv(file_clean, index=False)
    # Save the cleaned up DataFrame to an excel file without the index
    df2.to_excel('file_clean.xlsx', index=False)
  

5. Plot

• Ployting series using pandas

    # Create a plot with color='red'
    df.plot(color='red')
    # Add a title
    plt.title('Temperature in Austin')
    # Specify the x-axis label
    plt.xlabel('Hours since midnight August 1, 2010')
    # Specify the y-axis label
    plt.ylabel('Temperature (degrees F)')
    # Display the plot
    plt.show()
  

    #fix scales
    plt.yscale('log')
    #legend
    df.plot(legend=True)
    #axis
    plt.axis(('2001','2002',0,100))
    #saving plot
    plt.savefig()
  

• Plotting DataFrame

    # Plot all columns (default)
    df.plot()
    plt.show()
  
    # Plot all columns as subplots
    df.plot(subplots=True)
    plt.show()
  
    # Plot just the Dew Point data
    column_list1 = ['Dew Point (deg F)']
    df[column_list1].plot()
    plt.show()
    # Plot the Dew Point and Temperature data, but not the Pressure data
    column_list2 = ['Temperature (deg F)','Dew Point (deg F)']
    df[column_list2].plot()
    plt.show()
  

• Plots

        # s for size,c for color,alpha for opacity
    df.plot(kind='scatter',x='HP',y='MPG',s=sizes,alpha=,c=)
    df[cols].plot(kind='box',subplots=True)
        # Put the x-axis on a logarithmic scale
        plt.xscale('log')
  

    # Create a list of y-axis column names: y_columns
    y_columns = ['AAPL','IBM']
    df.plot(x='Month', y=y_columns)
    plt.title('Monthly stock prices')
    plt.ylabel('Price ($US)')
    plt.show()
        plt.hist(x,bins=int)
        # clears the entire current figure与其所有轴，但是窗口打开，这样它可以被重复使用其他地块。
        plt.clf()
  

    #plotting the PDF, only normed=True; CDF, cumulative=True in addition to normed=True
  
    # This formats the plots such that they appear on separate rows
    fig, axes = plt.subplots(nrows=2, ncols=1)
  
    # Plot the PDF
    df.fraction.plot(ax=axes[0], kind='hist', normed=True,bins=30, range=(0,.3))
    plt.show()
  
    # Plot the CDF
    df.fraction.plot(ax=axes[1], kind='hist', normed=True, bins=30, cumulative=True, range=(0,.3))
    plt.show()
  

  • Appendix

  #Histogram
  # Import matplotlib.pyplot
  import matplotlib.pyplot as plt
  # Describe the column
  df['Existing Zoning Sqft'].describe()
  # Plot the histogram
  df['Existing Zoning Sqft'].plot(kind='hist', rot=70, logx=True, logy=True)
  
  #Boxplot
  # Create the boxplot
  df.boxplot(column='initial_cost', by='Borough', rot=90)
  
  #Scatter Plot
  # Create and display the first scatter plot
  df.plot(kind='scatter', x='initial_cost', y='total_est_fee', rot=70)
  

6. Statistical exploratory data analysis

• pandas和numpy涉及到axis参数的问题

  参阅： 如何理解pandas中axis参数

    #summary stat of numerical cols of DataFrame
    df.describe()
    df.max()
    df.min()
    df.quantile([0.05,0.95])
    df.std()
  
    #axis='columns' computes the mean across all columns per row.
    mean = df.mean(axis='columns')
    mean.plot()
  

• Separating populations with Boolean indexing

    df['col'].unique()
    df[df['origin'] == 'US'].count()
  

  • 小case

      # Display the box plots on 3 separate rows and 1 column
      fig, axes = plt.subplots(nrows=3, ncols=1)
    
      # Generate a box plot of the fare prices for the First passenger class
      titanic.loc[titanic['pclass'] == 1].plot(ax=axes[0], y='fare', kind='box')
    
      # Generate a box plot of the fare prices for the Second passenger class
      titanic.loc[titanic['pclass'] == 2].plot(ax=axes[1], y='fare', kind='box')
    
      # Generate a box plot of the fare prices for the Third passenger class
      titanic.loc[titanic[ 'pclass']==3].plot(ax=axes[2], y='fare', kind='box')
    
      # Display the plot
      plt.show()
    

7. Time Series

• Creating DatetimeIndex

    # Prepare a format string: time_format
    time_format = '%Y-%m-%d %H:%M'
  
    # Convert date_list into a datetime object: my_datetimes
    my_datetimes = pd.to_datetime(date_list, format=time_format)
  
    # Construct a pandas Series using temperature_list and my_datetimes: time_series
    time_series = pd.Series(temperature_list, index=my_datetimes)
  

• Extracting data using datetime range

    ts1 = ts0.loc['2010-10-11 21:00:00':'2010-10-11 22:00:00']
    ts2 = ts0.loc['2010-07-04']
    ts3 = ts0.loc['2010-12-15':'2010-12-31']
  

• Reindexing

Input	Description
'min','T'	minute
'H'	hour
'D'	day
'B'	business day
'W'	week
'M'	month
'Q'	quarter
'A'	year

• Resampling time series

  Downsampling: reduce datetime rows to slower freq. day->week
  Upsampling: increase datetime rows to faster freq. day->hour

  df.resample('D').mean()
  df.resample('4H').ffill()
  

8. 防盗

Seaborn 参考

• Format Setting

  有5个seaborn的主题
  - darkgrid 黑色网格（默认）
  - whitegrid 白色网格
  - dark 黑色背景
  - white 白色背景
  - ticks 四周都有刻度线的白背景

  sns.set_style("whitegrid")
  

  画布大小

  plt.figure(figsize=(7,7))
  

  坐标轴倾斜45°

  plt.xticks(rotation=45)
  

• Bar Plot

  seaborn.barplot (x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=, ci=95, n_boot=1000, units=None, orient=None, color=None, palette=None, saturation=0.75, errcolor='.26', errwidth=None, capsize=None, dodge=True, ax=None, **kwargs)
  - x,y,hue : names of variable in data or vector data
  - data : DataFrame,array or list of array,optional
  - color :matplotlib color,optional
  - palette : palette name,list, or dict,optional 参考调色板教程
  - ax : matplotlib Axes,optional
  - hue: 类似于label

  #hue也可以换为['female','male']，palette=sns.cubehelix_palette(120)
  ax=sns.barplot(x=data.gender.value_counts().index,y=data.gender.value_counts(), hue=data['gender'].value_counts().index,palette="Greens_d")
  plt.legend(loc=8)
  plt.xlabel('Gender')
  plt.ylabel('Frequency') #另一种写法ax.set_ylabels()
  plt.title('Show of Gender Bar Plot')
  plt.show()
  

• Catplot 参考文章 （理解为高级汇总,kind='types')

  snsborn.despine

  plt.figure(figsize=(10,10))
  g=sns.catplot(x='gender',y='math score',data=data,hue='lunch',kind='bar',height=4, aspect=1)
  g.despine(left=True) #移除画布框线
  plt.tight_layout() #紧缩排列
  plt.show()
  

  f,ax=plt.subplots(figsize=(9,10))
  sns.barplot(y=data.gender.value_counts().index,x=data.gender.value_counts()
              ,label='gender',alpha=0.5,color='red')
  sns.barplot(y=data['race/ethnicity'].value_counts().index,x=data['race/ethnicity'].value_counts(),
                color='blue',label='race',alpha=0.7)
  ax.legend(loc='upper right',frameon=True)
  ax.set(xlabel='Gender , Race/Ethnicity',ylabel='Groups',title="Gender vs Race/Ethnicity ")
  plt.show()
  

• Point Plot

  seaborn.pointplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=, ci=95, n_boot=1000, units=None, markers='o', linestyles='-', dodge=False, join=True, scale=1, orient=None, color=None, palette=None, errwidth=None, capsize=None, ax=None, **kwargs)
  - x, y, hue : names of variables in data or vector data, optional
  - data : DataFrame, array, or list of arrays, optional
  - order, hue_order : lists of strings, optional
  - markers : string or list of strings, optional
  - linestyles : string or list of strings, optional
  - color : matplotlib color, optional
  - palette : palette name, list, or dict, optional
  - ax : matplotlib Axes, optional

  f,ax1=plt.subplots(figsize=(25,10))
  sns.pointplot(x=np.arange(1,191), y=data[(data['Race/Ethnicity']=='group B')].Math_Score,color='blue',alpha=0.6)
  sns.pointplot(x=np.arange(1,191),y=data[(data['Race/Ethnicity']=='group B')].Reading_Score,color='pink',alpha=0.2)
  ax1.set(xlabel='Group B index State',ylabel='Frequency',title="Group B Math Score & Reading_Score") #也可以用plt.xlabel
  plt.xticks(rotation=90) #轴旋转
  plt.grid() #加网线
  plt.show()
  

  ax=sns.pointplot(x='Writing_Score',y='Math_Score',hue='Gender',data=data,markers=["o", "x"],linestyles=["-", "--"])
  plt.legend(loc='lower right')
  plt.xticks(rotation=90)
  plt.show()
  

• Joint Plot 包含核密度估计 KDE图

  seaborn.jointplot(x, y, data=None, kind='scatter', stat_func=None, color=None, height=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None, joint_kws=None, marginal_kws=None, annot_kws=None, **kwargs)
  - x, y : strings or vectors
  - data : DataFrame, optional
  - kind : { “scatter” | “reg” | “resid” | “kde” | “hex” }, optional
  - color : matplotlib color, optional
  - dropna : bool, optional

  plt.figure(figsize=(10,10))
  #kind='kde' 核密度图
  sns.jointplot(x=np.arange(1,191),y=data[(data['Race/Ethnicity']=='group B')].Math_Score,color='lime',kind='hex',alpha=0.8)
  plt.xlabel('Group B index State')
  plt.ylabel('Frequency')
  plt.title('Group B Frequency Race/Ethnicity')
  plt.xticks(rotation=90)
  plt.tight_layout()
  plt.show()
  

  #散点图+KDE图
  data['Race/Ethnicity'].unique()
  len(data[(data['Race/Ethnicity']=='group B')].Math_Score)
  plt.figure(figsize=(10,10))
  sns.jointplot(x=np.arange(1,191),y=data[(data['Race/Ethnicity']=='group B')].Reading_Score,color='k').plot_joint(sns.kdeplot, zorder=0, n_levels=6)
  plt.xlabel('Group B index State')
  plt.ylabel('Frequency')
  plt.title('Group B Math Score & Reading_Score')
  plt.xticks(rotation=90)
  plt.show()
  

• KDE Plot (jointplot中可以kind='kde')

  seaborn.kdeplot(data, data2=None, shade=False, vertical=False, kernel='gau', bw='scott', gridsize=100, cut=3, clip=None, legend=True, cumulative=False, shade_lowest=True, cbar=False, cbar_ax=None, cbar_kws=None, ax=None, **kwargs)
  - data : 1d array-like
  - data2: 1d array-like, optional
  - shade : bool, optional
  - vertical : bool, optional
  - kernel : {‘gau’ | ‘cos’ | ‘biw’ | ‘epa’ | ‘tri’ | ‘triw’ }, optional
  - cut : scalar, optional
  - legend : bool, optional
  - ax : matplotlib axes, optional

• Pie chart

  plt.figure(figsize=(7,7))
  #explode是偏离轴心（可小数），shadow是阴影
  plt.pie(data['Race/Ethnicity'].value_counts().values,explode=[0,0,0.0,0,0.1],labels=data['Race/Ethnicity'].value_counts().index,colors=['pink','grey','yellow','lime','brown'],autopct='%1.1f%%',shadow=True)
  plt.title('Race/Ethnicity According Analysis',color='black',fontsize=10)
  plt.legend(data['Race/Ethnicity'].value_counts().index,loc=2)
  plt.axis('equal') #避免比例压缩为椭圆
  plt.show()
  

• LM Plot

  seaborn.lmplot(x, y, data, hue=None, col=None, row=None, palette=None, col_wrap=None, height=5, aspect=1, markers='o', sharex=True, sharey=True, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=True, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=False, x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None, size=None)
  - x, y : strings,options
  - data : DataFrame, optional
  - hue, col, row : strings
  - palette : palette name, list, or dict, optional
  - markers : matplotlib marker code or list of marker codes, optional
  - legend : bool, optional
  - scatter : bool, optional

  sns.lmplot(x='Math_Score',y='Writing_Score',data=data,hue='Gender',markers=['x','o'])
  plt.ylabel('Writing Score')
  plt.xlabel('Math Score')
  plt.show()
  

Practices

• 练习resampling

    import numpy as ny
    import pandas as pd
    import matplotlib.pyplot as plt
    df=pd.DataFrame(pd.read_csv('test.csv'))
  
    #给特定列重命名
    df=df.rename(columns ={'_c1':'date'})
  
    #删除where col_name is null的rows
    df1=df[df['date'].notnull()]
  
    #将目标列设置为index
    df1.set_index(['date'],inplace=True)
  
    #resample只能适用于datetimeindex，因此将index转为datetimeindex格式
    df1.index=pd.DatetimeIndex(df1.index)
  
    #对第二列开始的数据进行分类求和
    df1.iloc[:,1:].resample('W').sum()
  
    #对id分组再进行resampling
    df1.groupby('id').resample('W').sum().iloc[:,1:]