numpy、pandas、linear_regression需要

Numpy

simplifies representing arrays and performing linear algebra operations
import numpy as np

1. create an 8-element vector
   one_dimensional_array = np.array([1,2,3,4,5,6,7,8])
2. create a 3x2 matrix
   matrix = np.array([1,2],[3,4],[5,6])
3. all zeros/ones
   np.zeros,np.ones
4. populate an array with a sequence of numbers
   the lower bound 5, but not the upper bound 12
   sequence_of_integers = np.arange(5,12)
5. populate arrays with random numbers 6-element vectors
   random_integers_between_50_and_100 = np.random.randint(low=50, high=101, size=(6))
6. create random floating-point values between 0.0 and 1.0
   random_floats_between_0_and_1 = np.random.random([6])

pandas

provides an easy way to represent datasets in memory
import numpy as np
import pandas as pd
dataframes are central data structure in the pandas API, which stores data in cells and has named columns(usually) and numbered rows

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1. create a Dataframe

my_data = np.array([[0, 3], [10, 7], [20, 9], [30, 14], [40, 15]])
my_column_names = ['temperature', 'activity']
my_dataframe = pd.DataFrame(data=my_data, columns=my_column_names)

2. adding a new column to a Dataframe
   my_dataframe["adjusted"] = my_dataframe["activity"] + 2
   

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3. specifying a subset of a DataFrame
   my_dataframe.head(3) print the first 3 rows
   

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   my_dataframe.iloc[[2]] isolate the 2nd row
   my_dataframe[1:4] isolates the 1-4 row(starting from 0)
   my_dataframe['activity'] isolates the specific columns
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4. Referencing. If you assign a DataFrame to a new variable, any change to the DataFrame or to the new variable will be reflected in the other.
   reference_to_df = df
   print the value of a particular cell df['Jason'][1]
   print with format
   print(" Starting value of reference_to_df: %d\n" % reference_to_df['Jason'][1])
   modify a cell in df
   df.at[1, 'Jason'] = df['Jason'][1] + 5

5. Copying. If you call the pd.DataFrame.copy method, you create a true independent copy. Changes to the original DataFrame or to the copy will not be reflected in the other.
   copy_of_my_dataframe = my_dataframe.copy()

6. pd.read_csv(" ") load .csv document
   return DataFrame
   which remains a name for each column

Linear Regression

import pandas as pd
import tensorflow as tf
from matplotlib import  pyplot as plt

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