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app2.py
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app2.py
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import streamlit as st
from pathlib import Path
import base64
# Initial page config
# st.set_page_config(
# page_title='Datascience cheat sheet',
# layout="wide",
# initial_sidebar_state="expanded",
# )
def main():
cs_sidebar()
cs_body()
return None
# Thanks to streamlitopedia for the following code snippet
def img_to_bytes(img_path):
img_bytes = Path(img_path).read_bytes()
encoded = base64.b64encode(img_bytes).decode()
return encoded
# sidebar
def cs_sidebar():
#
#
# st.sidebar.markdown('Import convention')
# st.sidebar.code('>>> import streamlit as st')
#
# st.sidebar.markdown('__Add widgets to sidebar__')
# st.sidebar.code('''
# st.sidebar.<widget>
# >>> a = st.sidebar.radio(\'R:\',[1,2])
# ''')
#
# st.sidebar.markdown('__Command line__')
# st.sidebar.code('''
# $ streamlit --help
# $ streamlit run your_script.py
# $ streamlit hello
# $ streamlit config show
# $ streamlit cache clear
# $ streamlit docs
# $ streamlit --version
# ''')
#
# st.sidebar.markdown('__Pre-release features__')
# st.sidebar.markdown('[Beta and experimental features](https://docs.streamlit.io/en/0.70.0/api.html#beta-and-experimental-features)')
# st.sidebar.code('''
# pip uninstall streamlit
# pip install streamlit-nightly --upgrade
# ''')
# st.sidebar.markdown('''[<img src='data:image/png;base64,{}' class='img-fluid' width=32 height=32>](https://github.com/daniellewisDL/streamlit-cheat-sheet) <small>st.cheat_sheet v0.71.0 | Nov 2020</small>'''.format(img_to_bytes("brain.png")), unsafe_allow_html=True)
return None
##########################
# Main body of cheat sheet
##########################
#
def cs_body():
# Magic commands
st.markdown("""
<style>
body {
color: #00628B;
background-color: #D6EAF8;
}
</style>
""", unsafe_allow_html=True)
# st.title("Data Science Cheat App")
# type = ["Python Basics", "Pandas Basics", "Numpy Basics", "ML Algorithms"]
# st.sidebar.markdown('Choose the cheat sheet to learn')
# activity = st.sidebar.radio("Choose one from down", type)
# if "Python Basics" in activity:
st.subheader("Numpy Basics")
col1, col2, col3 = st.beta_columns(3)
col1.subheader('Creating Arrays')
col1.code('''
>>> a = np.array([1,2,3])
>>> b = np.array([(1.5,2,3), (4,5,6)], dtype = float)
>>> c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]],
dtype = float)
''')
# Display text
col1.subheader('Initial Placeholders')
col1.code('''
>>> np.zeros((3,4)) ------- Create an array of zeros
>>> np.ones((2,3,4),dtype=np.int16) ------- Create an array of ones
>>> d = np.arange(10,25,5) ------- Create an array of evenly
>>> np.linspace(0,2,9) ------- Create an array of evenly spaced values (number of samples)
>>> e = np.full((2,2),7) ------- Create a constant array
>>> f = np.eye(2) ------- Create a 2X2 identity matrix
>>> np.random.random((2,2)) ------- Create an array with random values
>>> np.empty((3,2)) ------- Create an empty array
''')
# Display data
col1.subheader('I/O')
col1.write('saving & Loading On Disk')
col1.code('''
>>> np.save('my_array', a)
>>> np.savez('array.npz', a, b)
>>> np.load('my_array.npy')
''')
# Display media
col1.write('Saving & Loading Text Files')
col1.code('''
>>> np.loadtxt("myfile.txt")
>>> np.genfromtxt("my_file.csv", delimiter=',')
>>> np.savetxt("myarray.txt", a, delimiter=" ")
''')
# Display interactive widgets
col1.subheader('Data Types')
col1.code('''
>>> np.int64 ------- Signed 64-bit integer types
>>> np.float32 ------- Standard double-precision floating point
>>> np.complex ------- Complex numbers represented by 128 floats
>>> np.bool ------- Boolean type storing TRUE and FALSE values
>>> np.object ------- Python object type
>>> np.string_ ------- Fixed-length string type
>>> np.unicode_ ------- Fixed-length unicode type
''')
col1.subheader('Inspecting Your Array')
col1.code('''
>>> a.shape ------- Array dimensions
>>> len(a) ------- Length of array
>>> b.ndim ------- Number of array dimensions
>>> e.size ------- Number of array elements
>>> b.dtype ------- Data type of array elements
>>> b.dtype.name ------- Name of data type
>>> b.astype(int) ------- Convert an array to a different type
''')
# Libraries
col1.subheader('Subsetting, Slicing, Indexing')
col1.write('Subsetting')
col1.code('''
>>> a[2] 1 2 3 ------- Select the element at the 2nd index
3
>>> b[1,2] ------- Select the element at row 1 column 2
1.5 2 3 6.0 ( equivalent to b[1][2])
''')
col2.write('Slicing')
col2.code('''
>>> a[0:2] ------- Select items at index 0 and 1
array([1, 2])
>>> b[0:2,1] ------- Select items at rows 0 and 1 in column 1
array([ 2., 5.])
>>> b[:1] ------- Select all items at row 0 (equivalent to b[0:1, :])
array([[1.5, 2., 3.]])
>>> c[1,...] ------- Same as [1,:,:]
array([[[3., 2., 1.],
[4., 5., 6.]]])
>>> a[ : :-1] ------- Reversed array a
array([3, 2, 1])
''')
# Placeholders, help, and options
col2.write('Boolean Indexing')
col2.code('''
>>> a[a<2] array([1]) ------- Select elements from a less than 2
''')
# Mutate data
col2.write('Fancy Indexing')
col2.code('''
>>> b[[1, 0, 1, 0],[0, 1, 2, 0]] ------- Select elements (1,0),(0,1),(1,2) and (0,0)
array([4.,2.,6.,1.5])
>>> b[[1, 0, 1, 0]][:,[0,1,2,0]] -------- Select a subset of the matrix’s rows and columns
array([[4.,5.,6.,4.], [1.5,2.,3.,1.5],
[4.,5.,6.,4.], [1.5,2.,3.,1.5]])
''')
col2.subheader('Array Mathematics')
col2.write('Arithmetic Operations')
col2.code('''
>>> g = a - b
array([[-0.5, 0. , 0. ], ------- Subtraction
[-3. , -3. , -3. ]])
>>> np.subtract(a,b) ------- Subtraction
>>> b + a ------- Addition
array([[ 2.5, 4. , 6. ],
[ 5. , 7. , 9. ]])
>>> np.add(b,a) ------- Addition
>>> a / b ------- Division
array([[ 0.66666667, 1.
, 1. ], , 0.5 ]])
>>> a * b ------- Multiplication
array([[ 1.5, 4. , 9. ],
[ 4. , 10. , 18. ]])
>>> np.multiply(a,b) ------- Multiplication
>>> np.exp(b) ------- Exponentiation
>>> np.sqrt(b) ------- Square root
>>> np.sin(a) ------- Print sines of an Array
>>> np.cos(b) ------- Element-wise cosine
>>> np.log(a) ------- Element-wise natural logarithm
>>> e.dot(f) ------- Dot product
array([[ 7., 7.], [ 7., 7.]])
''')
col2.write('Comparison')
col2.code('''
>> a == b
array([[False, True, True], ------- Element-wise comparison
[False, False, False]], dtype=bool)
>>> a < 2 ------- Element-wise comparison
array([True, False, False], dtype=bool)
>>> np.array_equal(a, b) ------- Array-wise comparison
''')
col3.write('Aggregate Functions')
col3.code('''
>>> a.sum() ------- Array-wise sum
>>> a.min() ------- Array-wise minimum value
>>> b.max(axis=0) ------- Maximum value of an array row
>>> b.cumsum(axis=1) ------- Cumulative sum of the elements
>>> a.mean() ------- Mean
>>> b.median() ------- Median
>>> a.corrcoef() ------- Correlation coefficient
>>> np.std(b) ------- Standard deviation
''')
# Control flow
col3.subheader('Copy Arrays')
col3.code('''
>>> h = a.view() ------- Create a view of the array with the same data
>>> np.copy(a) ------- Create a copy of the array
>>> h = a.copy() ------- Create a deep copy of the array
''')
# Lay out your app
col3.subheader('Sorting Arrays')
col3.code('''
>>> a.sort() ------- Sort an array
>>> c.sort(axis=0) ------- Sort the elements of an array's axis
''')
col3.subheader('Array Manipulation')
col3.write('Transposing Array')
col3.code('''
>>> i = np.transpose(b) ------- Permute array dimensions
>>> i.T ------- Permute array dimensions
''')
col3.write('Changing Array Shape')
col3.code('''
>>> b.ravel() ------- Flatten the array
>>> g.reshape(3,-2) ------- Reshape but don't change the data
''')
col3.write('Adding/Removing Elements')
col3.code('''
>>> h.resize((2,6)) ------- Return a new array with shape (2,6)
>>> np.append(h,g) ------- Append items to an array
>>> np.insert(a, 1, 5) ------- Insert items in an array
>>> np.delete(a,[1]) ------- Delete items from an array
''')
# Optimize performance
col3.write('Combining Array')
col3.code('''
>>> np.concatenate((a,d),axis=0) ------- Concatenate arrays
array([ 1, 2, 3, 10, 15, 20])
>>> np.vstack((a,b)) ------- Stack arrays vertically (row-wise)
>>> np.r_[e,f] ------- Stack arrays vertically (row-wise)
>>> np.hstack((e,f)) ------- Stack arrays horizontally (column-wise)
array([[ 7., 7., 1., 0.],
[7., 7., 0., 1.,]])
>>> np.column_stack((a,d)) ------- Create stacked column-wise arrays
array([[ 1, 10],
[ 2, 15],
[ 3, 20]])
>>> np.c_[a,d] ------- Create stacked column-wise arrays
''')
col3.write('Splitting Array')
col3.code('''
>>> np.hsplit(a,3) ------- Split the array horizontally at the 3rd index
[array([1]),array([2]),array([3])]
>>> np.vsplit(c,2) ------- Split the array vertically at the 2nd index
[array([[[ 1.5, 2. , 1. ],
[ 4. , 5. , 6. ]]]),
array([[[ 3., 2., 3.],
[ 4., 5., 6.]]])]
''')
return None
# Run main()
if __name__ == '__main__':
main()