用Python导入数据（二）

有许多数据类型无法轻松保存到平面文件，例如列表和词典。如果希望文件是人类可读的，可能希望以巧妙的方式将它们保存为文本文件。JSON适用于Python词典。

但是，如果只想将它们导入Python，则可以序列化它们。所有这些意味着将对象转换为字节序列或字节流。

1、导入pickle包，从文件中打开以前的pickle数据结构并加载它。

# Import pickle package
import pickle

# Open pickle file and load data: d
with open('data.pkl', 'rb') as file:
    d = pickle.load(file)

# Print d
print(d)

# Print datatype of d
print(type(d))

{'Airline': '8', 'Aug': '85', 'Mar': '84.4', 'June': '69.4'}
<class 'dict'>

2、导入Excel电子表格

该excel电子表格包含两个工作表‘2002’和‘2004’

使用pandas导入Excel电子表格以及在任何已加载的.xlsx文件中列出工作表的名称

# Import pandas
import pandas as pd

# Assign spreadsheet filename: file
file = 'battledeath.xlsx'

# Load spreadsheet: xl
xl = pd.ExcelFile(file)

print(xl)

# Print sheet names
print(xl.sheet_names)

<pandas.io.excel.ExcelFile object at 0x7f78e91bb2b0>
['2002', '2004']

将加载的.xlsx文件的任何给定工作表导入为DataFrame。可以通过指定工作表的名称或索引来完成此操作。

# Load a sheet into a DataFrame by name: df1
df1 = xl.parse('2004')

# Print the head of the DataFrame df1
print(df1.head())

# Load a sheet into a DataFrame by index: df2
df2 = xl.parse(0)

# Print the head of the DataFrame df2
print(df2.head())

War(country) 2004
0 Afghanistan 9.451028
1 Albania 0.130354
2 Algeria 3.407277
3 Andorra 0.000000
4 Angola 2.597931
War, age-adjusted mortality due to 2002
0 Afghanistan 36.083990
1 Albania 0.128908
2 Algeria 18.314120
3 Andorra 0.000000
4 Angola 18.964560

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自定义电子表格导入

和以前一样，你将使用方法parse（）。但是，这一次，将添加其他参数skiprows：跳过行，names：命名列，parse_cols：指定要解析的列。可以根据需要将所有这些参数分配给包含特定行号，字符串和列号的列表

# Parse the first sheet and rename the columns: df1
df1 = xl.parse(0, skiprows=[0], names=['Country','AAM due to War (2002)'])

# Print the head of the DataFrame df1
print(df1.head())

# Parse the first column of the second sheet and rename the column: df2
df2 = xl.parse(1, parse_cols=[0], skiprows=[0], names=['Country'])

# Print the head of the DataFrame df2
print(df2.head())

Country AAM due to War (2002)
0 Albania 0.128908
1 Algeria 18.314120
2 Andorra 0.000000
3 Angola 18.964560
4 Antigua and Barbuda 0.000000

Country
0 Albania
1 Algeria
2 Andorra
3 Angola
4 Antigua and Barbuda

3、导入SAS文件

sas是统计分析文件。用于数据流统计。

import pandas as pd
import matplotlib.pyplot as plt

# Import sas7bdat package
from sas7bdat import SAS7BDAT

# Save file to a DataFrame: df_sas
with SAS7BDAT('sales.sas7bdat') as file:
    df_sas = SAS7BDAT.to_data_frame(file)

# Print head of DataFrame
print(df_sas.head())

# Plot histogram of DataFrame features (pandas and pyplot already imported)
pd.DataFrame.hist(df_sas[['P']])
plt.ylabel('count')
plt.show()

YEAR P S
0 1950.0 12.9 181.899994
1 1951.0 11.9 245.000000
2 1952.0 10.7 250.199997
3 1953.0 11.3 265.899994
4 1954.0 11.2 248.500000

4、导入Stata文件

# Import pandas
import pandas as pd

# Load Stata file into a pandas DataFrame: df
df = pd.read_stata('disarea.dta')

# Print the head of the DataFrame df
print(df.head())

# Plot histogram of one column of the DataFrame
pd.DataFrame.hist(df[['disa10']])
plt.xlabel('Extent of disease')
plt.ylabel('Number of countries')
plt.show()

5、使用h5py导入HDF5文件

# Import packages
import numpy as np
import h5py

# Assign filename: file
file = 'LIGO_data.hdf5'

# Load file: data
data = h5py.File(file, 'r')

# Print the datatype of the loaded file
print(type(data))

# Print the keys of the file
for key in data.keys():
    print(key)

<class 'h5py._hl.files.File'>
meta
quality
strain

从HDF5文件中提取数据

# Get the HDF5 group: group
group = data['strain']

# Check out keys of group
for key in group.keys():
    print(key)

# Set variable equal to time series data: strain
strain = data['strain']['Strain'].value

# Set number of time points to sample: num_samples
num_samples = 10000

# Set time vector
time = np.arange(0, 1, 1/num_samples)

# Plot data
plt.plot(time, strain[:num_samples])
plt.xlabel('GPS Time (s)')
plt.ylabel('strain')
plt.show()

6、加载.mat文件

# Import package
import scipy.io

import matplotlib.pyplot as plt
import numpy as np

# Load MATLAB file: mat
mat = scipy.io.loadmat('albeck_gene_expression.mat')

# Print the datatype type of mat
print(type(mat))

# Print the keys of the MATLAB dictionary
print(mat.keys())

# Print the type of the value corresponding to the key 'CYratioCyt'
print(type(mat['CYratioCyt']))

# Print the shape of the value corresponding to the key 'CYratioCyt'
print(np.shape(mat['CYratioCyt']))

# Subset the array and plot it
data = mat['CYratioCyt'][25, 5:]

fig = plt.figure()
plt.plot(data)
plt.xlabel('time (min.)')
plt.ylabel('normalized fluorescence (measure of expression)')
plt.show()

dict_keys(['__globals__', '__version__', '__header__', 'cfpCyt', 'rfpCyt', 'cfpNuc', 'rfpNuc', 'CYratioCyt', 'yfpCyt', 'yfpNuc'])
<class 'numpy.ndarray'>
(200, 137)

用Python导入数据（二）

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