Series index ： Introduction to data analysis | kaggle Titanic mission

List of articles

• One 、 Exploratory data analysis
• • （1） Create a simulation data
  • （2） Sort
  • （3） utilize Pandas Do arithmetic

One 、 Exploratory data analysis

Mainly introduce the use of Pandas Sort 、 Arithmetic calculation and calculation description function describe() Use .

（1） Create a simulation data

# Build a digital DataFrame data 
frame = pd.DataFrame(np.arange(8).reshape((2, 4)), 
                     index=['2', '1'], 
                     columns=['d', 'a', 'b', 'c'])
frame

pd.DataFrame()： Create a DataFrame object

np.arange(8).reshape((2, 4)) : Generate a two-dimensional array （2*4）, First column ：0,1,2,3 Second column ：4,5,6,7

index=['2, 1] ：DataFrame The index column of the object

columns=['d', 'a', 'b', 'c']：DataFrame The index line of the object

（2） Sort

frame.sort_values(by='c', ascending=True) #by The parameter points to the column to be arranged ,sacending Point sort method （ Ascending / Descending ）
#  Let the row index sort in ascending order 
frame.sort_index()
#  Let the column index sort in ascending order 
frame.sort_index(axis=1)
#  Sort the column index in descending order 
frame.sort_index(axis=1, ascending=False)
#  Let any two columns of data be sorted in descending order at the same time 
frame.sort_values(by=['a', 'c'], ascending=False)

 When sorting two columns , In order , If there are equal numbers in the former , Sort by the next column .
 Such as ：sort_values(by=['a','c'].ascending = [False, True])
 This line means to follow a Arrange in descending order , When a Press the same value in b Ascending order .

（3） utilize Pandas Do arithmetic

frame1_a = pd.DataFrame(np.arange(9.).reshape(3, 3),
                     columns=['a', 'b', 'c'],
                     index=['one', 'two', 'three'])
frame1_b = pd.DataFrame(np.arange(12.).reshape(4, 3),
                     columns=['a', 'e', 'c'],
                     index=['first', 'one', 'two', 'second'])
frame1_a

# take frame_a and frame_b Add additivity 
frame1_a + frame1_b

【 remind 】 Two DataFrame Add up , Will return a new DataFrame, The corresponding row and column values are added , If there is no corresponding, it will become null NaN.

Of course ,DataFrame There are many arithmetic operations , Such as subtraction , Division, etc , Interested students can see 《 utilize Python Data analysis 》 The fifth chapter Align arithmetic operations with data part , Find more relevant learning materials on the Internet .

#  call  describe  function , Observe frame2 Basic information of data 

frame2.describe()

''' count :  Sample data size  mean :  The average of the sample data  std :  Standard deviation of sample data  min :  Minimum value of sample data  25% :  Sample data 25% The value at the time of  50% :  Sample data 50% The value at the time of  75% :  Sample data 75% The value at the time of  max :  Maximum value of sample data  '''

'''  Look at the Titanic dataset   The fare   The basic statistics of this column  '''
text[' The fare '].describe()

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Last one ： Introduction to data analysis | kaggle Titanic mission （ Two ）—＞pandas Basics
Next ： Introduction to data analysis | kaggle Titanic mission （ Four ）—＞ Data cleaning and feature processing

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