Python For Data Science Cheat Sheet Lists Also see NumPy Arrays Libraries
>>> a = 'is' Import libraries
Python Basics >>> b = 'nice' >>> import numpy Data analysis Machine learning
Learn More Python for Data Science Interactively at www.datacamp.com >>> my_list = ['my', 'list', a, b] >>> import numpy as np
>>> my_list2 = [[4,5,6,7], [3,4,5,6]] Selective import
>>> from math import pi Scientific computing 2D plotting
Variables and Data Types Selecting List Elements Index starts at 0
Subset Install Python
Variable Assignment
>>> my_list[1] Select item at index 1
>>> x=5
>>> my_list[-3] Select 3rd last item
>>> x
Slice
5 >>> my_list[1:3] Select items at index 1 and 2
Calculations With Variables >>> my_list[1:] Select items after index 0
>>> my_list[:3] Select items before index 3 Leading open data science platform Free IDE that is included Create and share
>>> x+2 Sum of two variables
>>> my_list[:] Copy my_list powered by Python with Anaconda documents with live code,
7 visualizations, text, ...
>>> x-2 Subtraction of two variables
Subset Lists of Lists
>>> my_list2[1][0] my_list[list][itemOfList]
3
>>> my_list2[1][:2] Numpy Arrays Also see Lists
>>> x*2 Multiplication of two variables
>>> my_list = [1, 2, 3, 4]
10 List Operations >>> my_array = np.array(my_list)
>>> x**2 Exponentiation of a variable
25 >>> my_list + my_list >>> my_2darray = np.array([[1,2,3],[4,5,6]])
>>> x%2 Remainder of a variable ['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']
Selecting Numpy Array Elements Index starts at 0
1 >>> my_list * 2
>>> x/float(2) Division of a variable ['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice'] Subset
2.5 >>> my_list2 > 4 >>> my_array[1] Select item at index 1
True 2
Types and Type Conversion Slice
List Methods >>> my_array[0:2] Select items at index 0 and 1
str() '5', '3.45', 'True' Variables to strings
my_list.index(a) Get the index of an item array([1, 2])
>>>
int() 5, 3, 1 Variables to integers >>> my_list.count(a) Count an item Subset 2D Numpy arrays
>>> my_list.append('!') Append an item at a time >>> my_2darray[:,0] my_2darray[rows, columns]
my_list.remove('!') Remove an item array([1, 4])
float() 5.0, 1.0 Variables to floats >>>
>>> del(my_list[0:1]) Remove an item Numpy Array Operations
bool() True, True, True >>> my_list.reverse() Reverse the list
Variables to booleans >>> my_array > 3
>>> my_list.extend('!') Append an item array([False, False, False, True], dtype=bool)
>>> my_list.pop(-1) Remove an item >>> my_array * 2
Asking For Help >>> my_list.insert(0,'!') Insert an item array([2, 4, 6, 8])
>>> help(str) >>> my_list.sort() Sort the list >>> my_array + np.array([5, 6, 7, 8])
array([6, 8, 10, 12])
Strings
>>> my_string = 'thisStringIsAwesome' Numpy Array Functions
String Operations Index starts at 0
>>> my_string >>> my_array.shape Get the dimensions of the array
'thisStringIsAwesome' >>> my_string[3] >>> np.append(other_array) Append items to an array
>>> my_string[4:9] >>> np.insert(my_array, 1, 5) Insert items in an array
String Operations >>> np.delete(my_array,[1]) Delete items in an array
String Methods >>> np.mean(my_array) Mean of the array
>>> my_string * 2
'thisStringIsAwesomethisStringIsAwesome' >>> my_string.upper() String to uppercase >>> np.median(my_array) Median of the array
>>> my_string + 'Innit' >>> my_string.lower() String to lowercase >>> my_array.corrcoef() Correlation coefficient
'thisStringIsAwesomeInnit' >>> my_string.count('w') Count String elements >>> np.std(my_array) Standard deviation
>>> 'm' in my_string >>> my_string.replace('e', 'i') Replace String elements
True >>> my_string.strip() Strip whitespaces DataCamp
Learn Python for Data Science Interactively
, Python For Data Science Cheat Sheet Advanced Indexing Also see NumPy Arrays Combining Data
Selecting data1 data2
Pandas >>> df3.loc[:,(df3>1).any()] Select cols with any vals >1 X1 X2 X1 X3
Learn Python for Data Science Interactively at www.DataCamp.com >>> df3.loc[:,(df3>1).all()] Select cols with vals > 1
>>> df3.loc[:,df3.isnull().any()] Select cols with NaN a 11.432 a 20.784
>>> df3.loc[:,df3.notnull().all()] Select cols without NaN b 1.303 b NaN
Indexing With isin c 99.906 d 20.784
>>> df[(df.Country.isin(df2.Type))] Find same elements
Reshaping Data >>> df3.filter(items=”a”,”b”]) Filter on values
Merge
>>> df.select(lambda x: not x%5) Select specific elements
Pivot Where X1 X2 X3
>>> pd.merge(data1,
>>> df3= df2.pivot(index='Date', Spread rows into columns >>> s.where(s > 0) Subset the data data2, a 11.432 20.784
columns='Type', Query how='left',
values='Value') b 1.303 NaN
>>> df6.query('second > first') Query DataFrame on='X1')
c 99.906 NaN
Date Type Value
0 2016-03-01 a 11.432 Type a b c Setting/Resetting Index >>> pd.merge(data1, X1 X2 X3
1 2016-03-02 b 13.031 Date data2, a 11.432 20.784
>>> df.set_index('Country') Set the index
how='right',
2 2016-03-01 c 20.784 2016-03-01 11.432 NaN 20.784 >>> df4 = df.reset_index() Reset the index b 1.303 NaN
on='X1')
3 2016-03-03 a 99.906 >>> df = df.rename(index=str, Rename DataFrame d NaN 20.784
2016-03-02 1.303 13.031 NaN columns={"Country":"cntry",
4 2016-03-02 a 1.303 "Capital":"cptl", >>> pd.merge(data1,
2016-03-03 99.906 NaN 20.784 "Population":"ppltn"}) X1 X2 X3
5 2016-03-03 c 20.784 data2,
how='inner', a 11.432 20.784
Pivot Table Reindexing on='X1') b 1.303 NaN
>>> s2 = s.reindex(['a','c','d','e','b'])
>>> df4 = pd.pivot_table(df2, Spread rows into columns X1 X2 X3
values='Value', Forward Filling Backward Filling >>> pd.merge(data1,
index='Date', data2, a 11.432 20.784
columns='Type']) >>> df.reindex(range(4), >>> s3 = s.reindex(range(5), how='outer', b 1.303 NaN
method='ffill') method='bfill') on='X1') c 99.906 NaN
Stack / Unstack Country Capital Population 0 3
0 Belgium Brussels 11190846 1 3 d NaN 20.784
>>> stacked = df5.stack() Pivot a level of column labels 1 India New Delhi 1303171035 2 3
>>> stacked.unstack() Pivot a level of index labels 2 Brazil Brasília 207847528 3 3 Join
3 Brazil Brasília 207847528 4 3
0 1 1 5 0 0.233482 >>> data1.join(data2, how='right')
1 5 0.233482 0.390959 1 0.390959 MultiIndexing Concatenate
2 4 0.184713 0.237102 2 4 0 0.184713
>>> arrays = [np.array([1,2,3]),
3 3 0.433522 0.429401 1 0.237102 np.array([5,4,3])] Vertical
>>> df5 = pd.DataFrame(np.random.rand(3, 2), index=arrays) >>> s.append(s2)
Unstacked 3 3 0 0.433522
>>> tuples = list(zip(*arrays)) Horizontal/Vertical
1 0.429401 >>> index = pd.MultiIndex.from_tuples(tuples, >>> pd.concat([s,s2],axis=1, keys=['One','Two'])
Stacked names=['first', 'second']) >>> pd.concat([data1, data2], axis=1, join='inner')
>>> df6 = pd.DataFrame(np.random.rand(3, 2), index=index)
Melt >>> df2.set_index(["Date", "Type"])
>>> pd.melt(df2, Gather columns into rows
Dates
id_vars=["Date"],
value_vars=["Type", "Value"],
Duplicate Data >>> df2['Date']= pd.to_datetime(df2['Date'])
>>> df2['Date']= pd.date_range('2000-1-1',
value_name="Observations") >>> s3.unique() Return unique values periods=6,
>>> df2.duplicated('Type') Check duplicates freq='M')
Date Type Value
Date Variable Observations >>> dates = [datetime(2012,5,1), datetime(2012,5,2)]
0 2016-03-01 Type a >>> df2.drop_duplicates('Type', keep='last') Drop duplicates >>> index = pd.DatetimeIndex(dates)
0 2016-03-01 a 11.432 1 2016-03-02 Type b
>>> df.index.duplicated() Check index duplicates >>> index = pd.date_range(datetime(2012,2,1), end, freq='BM')
1 2016-03-02 b 13.031 2 2016-03-01 Type c
2 2016-03-01 c 20.784 3 2016-03-03 Type a Grouping Data Visualization Also see Matplotlib
4 2016-03-02 Type a
3 2016-03-03 a 99.906
5 2016-03-03 Type c Aggregation >>> import matplotlib.pyplot as plt
4 2016-03-02 a 1.303 >>> df2.groupby(by=['Date','Type']).mean()
6 2016-03-01 Value 11.432 >>> s.plot() >>> df2.plot()
>>> df4.groupby(level=0).sum()
5 2016-03-03 c 20.784 7 2016-03-02 Value 13.031 >>> df4.groupby(level=0).agg({'a':lambda x:sum(x)/len(x), >>> plt.show() >>> plt.show()
8 2016-03-01 Value 20.784 'b': np.sum})
9 2016-03-03 Value 99.906 Transformation
>>> customSum = lambda x: (x+x%2)
10 2016-03-02 Value 1.303
>>> df4.groupby(level=0).transform(customSum)
11 2016-03-03 Value 20.784
Iteration Missing Data
>>> df.dropna() Drop NaN values
>>> df.iteritems() (Column-index, Series) pairs >>> df3.fillna(df3.mean()) Fill NaN values with a predetermined value
>>> df.iterrows() (Row-index, Series) pairs >>> df2.replace("a", "f") Replace values with others
DataCamp
Learn Python for Data Science Interactively
>>> a = 'is' Import libraries
Python Basics >>> b = 'nice' >>> import numpy Data analysis Machine learning
Learn More Python for Data Science Interactively at www.datacamp.com >>> my_list = ['my', 'list', a, b] >>> import numpy as np
>>> my_list2 = [[4,5,6,7], [3,4,5,6]] Selective import
>>> from math import pi Scientific computing 2D plotting
Variables and Data Types Selecting List Elements Index starts at 0
Subset Install Python
Variable Assignment
>>> my_list[1] Select item at index 1
>>> x=5
>>> my_list[-3] Select 3rd last item
>>> x
Slice
5 >>> my_list[1:3] Select items at index 1 and 2
Calculations With Variables >>> my_list[1:] Select items after index 0
>>> my_list[:3] Select items before index 3 Leading open data science platform Free IDE that is included Create and share
>>> x+2 Sum of two variables
>>> my_list[:] Copy my_list powered by Python with Anaconda documents with live code,
7 visualizations, text, ...
>>> x-2 Subtraction of two variables
Subset Lists of Lists
>>> my_list2[1][0] my_list[list][itemOfList]
3
>>> my_list2[1][:2] Numpy Arrays Also see Lists
>>> x*2 Multiplication of two variables
>>> my_list = [1, 2, 3, 4]
10 List Operations >>> my_array = np.array(my_list)
>>> x**2 Exponentiation of a variable
25 >>> my_list + my_list >>> my_2darray = np.array([[1,2,3],[4,5,6]])
>>> x%2 Remainder of a variable ['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']
Selecting Numpy Array Elements Index starts at 0
1 >>> my_list * 2
>>> x/float(2) Division of a variable ['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice'] Subset
2.5 >>> my_list2 > 4 >>> my_array[1] Select item at index 1
True 2
Types and Type Conversion Slice
List Methods >>> my_array[0:2] Select items at index 0 and 1
str() '5', '3.45', 'True' Variables to strings
my_list.index(a) Get the index of an item array([1, 2])
>>>
int() 5, 3, 1 Variables to integers >>> my_list.count(a) Count an item Subset 2D Numpy arrays
>>> my_list.append('!') Append an item at a time >>> my_2darray[:,0] my_2darray[rows, columns]
my_list.remove('!') Remove an item array([1, 4])
float() 5.0, 1.0 Variables to floats >>>
>>> del(my_list[0:1]) Remove an item Numpy Array Operations
bool() True, True, True >>> my_list.reverse() Reverse the list
Variables to booleans >>> my_array > 3
>>> my_list.extend('!') Append an item array([False, False, False, True], dtype=bool)
>>> my_list.pop(-1) Remove an item >>> my_array * 2
Asking For Help >>> my_list.insert(0,'!') Insert an item array([2, 4, 6, 8])
>>> help(str) >>> my_list.sort() Sort the list >>> my_array + np.array([5, 6, 7, 8])
array([6, 8, 10, 12])
Strings
>>> my_string = 'thisStringIsAwesome' Numpy Array Functions
String Operations Index starts at 0
>>> my_string >>> my_array.shape Get the dimensions of the array
'thisStringIsAwesome' >>> my_string[3] >>> np.append(other_array) Append items to an array
>>> my_string[4:9] >>> np.insert(my_array, 1, 5) Insert items in an array
String Operations >>> np.delete(my_array,[1]) Delete items in an array
String Methods >>> np.mean(my_array) Mean of the array
>>> my_string * 2
'thisStringIsAwesomethisStringIsAwesome' >>> my_string.upper() String to uppercase >>> np.median(my_array) Median of the array
>>> my_string + 'Innit' >>> my_string.lower() String to lowercase >>> my_array.corrcoef() Correlation coefficient
'thisStringIsAwesomeInnit' >>> my_string.count('w') Count String elements >>> np.std(my_array) Standard deviation
>>> 'm' in my_string >>> my_string.replace('e', 'i') Replace String elements
True >>> my_string.strip() Strip whitespaces DataCamp
Learn Python for Data Science Interactively
, Python For Data Science Cheat Sheet Advanced Indexing Also see NumPy Arrays Combining Data
Selecting data1 data2
Pandas >>> df3.loc[:,(df3>1).any()] Select cols with any vals >1 X1 X2 X1 X3
Learn Python for Data Science Interactively at www.DataCamp.com >>> df3.loc[:,(df3>1).all()] Select cols with vals > 1
>>> df3.loc[:,df3.isnull().any()] Select cols with NaN a 11.432 a 20.784
>>> df3.loc[:,df3.notnull().all()] Select cols without NaN b 1.303 b NaN
Indexing With isin c 99.906 d 20.784
>>> df[(df.Country.isin(df2.Type))] Find same elements
Reshaping Data >>> df3.filter(items=”a”,”b”]) Filter on values
Merge
>>> df.select(lambda x: not x%5) Select specific elements
Pivot Where X1 X2 X3
>>> pd.merge(data1,
>>> df3= df2.pivot(index='Date', Spread rows into columns >>> s.where(s > 0) Subset the data data2, a 11.432 20.784
columns='Type', Query how='left',
values='Value') b 1.303 NaN
>>> df6.query('second > first') Query DataFrame on='X1')
c 99.906 NaN
Date Type Value
0 2016-03-01 a 11.432 Type a b c Setting/Resetting Index >>> pd.merge(data1, X1 X2 X3
1 2016-03-02 b 13.031 Date data2, a 11.432 20.784
>>> df.set_index('Country') Set the index
how='right',
2 2016-03-01 c 20.784 2016-03-01 11.432 NaN 20.784 >>> df4 = df.reset_index() Reset the index b 1.303 NaN
on='X1')
3 2016-03-03 a 99.906 >>> df = df.rename(index=str, Rename DataFrame d NaN 20.784
2016-03-02 1.303 13.031 NaN columns={"Country":"cntry",
4 2016-03-02 a 1.303 "Capital":"cptl", >>> pd.merge(data1,
2016-03-03 99.906 NaN 20.784 "Population":"ppltn"}) X1 X2 X3
5 2016-03-03 c 20.784 data2,
how='inner', a 11.432 20.784
Pivot Table Reindexing on='X1') b 1.303 NaN
>>> s2 = s.reindex(['a','c','d','e','b'])
>>> df4 = pd.pivot_table(df2, Spread rows into columns X1 X2 X3
values='Value', Forward Filling Backward Filling >>> pd.merge(data1,
index='Date', data2, a 11.432 20.784
columns='Type']) >>> df.reindex(range(4), >>> s3 = s.reindex(range(5), how='outer', b 1.303 NaN
method='ffill') method='bfill') on='X1') c 99.906 NaN
Stack / Unstack Country Capital Population 0 3
0 Belgium Brussels 11190846 1 3 d NaN 20.784
>>> stacked = df5.stack() Pivot a level of column labels 1 India New Delhi 1303171035 2 3
>>> stacked.unstack() Pivot a level of index labels 2 Brazil Brasília 207847528 3 3 Join
3 Brazil Brasília 207847528 4 3
0 1 1 5 0 0.233482 >>> data1.join(data2, how='right')
1 5 0.233482 0.390959 1 0.390959 MultiIndexing Concatenate
2 4 0.184713 0.237102 2 4 0 0.184713
>>> arrays = [np.array([1,2,3]),
3 3 0.433522 0.429401 1 0.237102 np.array([5,4,3])] Vertical
>>> df5 = pd.DataFrame(np.random.rand(3, 2), index=arrays) >>> s.append(s2)
Unstacked 3 3 0 0.433522
>>> tuples = list(zip(*arrays)) Horizontal/Vertical
1 0.429401 >>> index = pd.MultiIndex.from_tuples(tuples, >>> pd.concat([s,s2],axis=1, keys=['One','Two'])
Stacked names=['first', 'second']) >>> pd.concat([data1, data2], axis=1, join='inner')
>>> df6 = pd.DataFrame(np.random.rand(3, 2), index=index)
Melt >>> df2.set_index(["Date", "Type"])
>>> pd.melt(df2, Gather columns into rows
Dates
id_vars=["Date"],
value_vars=["Type", "Value"],
Duplicate Data >>> df2['Date']= pd.to_datetime(df2['Date'])
>>> df2['Date']= pd.date_range('2000-1-1',
value_name="Observations") >>> s3.unique() Return unique values periods=6,
>>> df2.duplicated('Type') Check duplicates freq='M')
Date Type Value
Date Variable Observations >>> dates = [datetime(2012,5,1), datetime(2012,5,2)]
0 2016-03-01 Type a >>> df2.drop_duplicates('Type', keep='last') Drop duplicates >>> index = pd.DatetimeIndex(dates)
0 2016-03-01 a 11.432 1 2016-03-02 Type b
>>> df.index.duplicated() Check index duplicates >>> index = pd.date_range(datetime(2012,2,1), end, freq='BM')
1 2016-03-02 b 13.031 2 2016-03-01 Type c
2 2016-03-01 c 20.784 3 2016-03-03 Type a Grouping Data Visualization Also see Matplotlib
4 2016-03-02 Type a
3 2016-03-03 a 99.906
5 2016-03-03 Type c Aggregation >>> import matplotlib.pyplot as plt
4 2016-03-02 a 1.303 >>> df2.groupby(by=['Date','Type']).mean()
6 2016-03-01 Value 11.432 >>> s.plot() >>> df2.plot()
>>> df4.groupby(level=0).sum()
5 2016-03-03 c 20.784 7 2016-03-02 Value 13.031 >>> df4.groupby(level=0).agg({'a':lambda x:sum(x)/len(x), >>> plt.show() >>> plt.show()
8 2016-03-01 Value 20.784 'b': np.sum})
9 2016-03-03 Value 99.906 Transformation
>>> customSum = lambda x: (x+x%2)
10 2016-03-02 Value 1.303
>>> df4.groupby(level=0).transform(customSum)
11 2016-03-03 Value 20.784
Iteration Missing Data
>>> df.dropna() Drop NaN values
>>> df.iteritems() (Column-index, Series) pairs >>> df3.fillna(df3.mean()) Fill NaN values with a predetermined value
>>> df.iterrows() (Row-index, Series) pairs >>> df2.replace("a", "f") Replace values with others
DataCamp
Learn Python for Data Science Interactively
