Numpy and Pandas for 2D data

2D Numpy array¶

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
# Subway ridership for 5 stations on 10 different days
ridership = np.array([
    [   0,    0,    2,    5,    0],
    [1478, 3877, 3674, 2328, 2539],
    [1613, 4088, 3991, 6461, 2691],
    [1560, 3392, 3826, 4787, 2613],
    [1608, 4802, 3932, 4477, 2705],
    [1576, 3933, 3909, 4979, 2685],
    [  95,  229,  255,  496,  201],
    [   2,    0,    1,   27,    0],
    [1438, 3785, 3589, 4174, 2215],
    [1342, 4043, 4009, 4665, 3033]
])

Accessing Numpy array:¶

In [2]:

print(ridership[1,4])  # In Python list of lists, we can access using a[i][j]. But, in numpy, it's a[i,j]

2539

In [3]:

print(len(ridership))

10

In [4]:

print(len(ridership[0]))

5

In [5]:

print(ridership[1:6,0:4])

[[1478 3877 3674 2328]
 [1613 4088 3991 6461]
 [1560 3392 3826 4787]
 [1608 4802 3932 4477]
 [1576 3933 3909 4979]]

vectorized operations¶

In [6]:

a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])

In [7]:

print(a)
print(b)

[[1 2 3]
 [4 5 6]
 [7 8 9]]
[[1 1 1]
 [2 2 2]
 [3 3 3]]

In [8]:

print(a+b)

[[ 2  3  4]
 [ 6  7  8]
 [10 11 12]]

In [9]:

first_col = ridership[:,0:1]

In [10]:

print(first_col)

[[   0]
 [1478]
 [1613]
 [1560]
 [1608]
 [1576]
 [  95]
 [   2]
 [1438]
 [1342]]

In [11]:

print(first_col.mean())

1071.2

In [12]:

print(ridership.mean(axis=0)) #mean for column

[ 1071.2  2814.9  2718.8  3239.9  1868.2]

In [13]:

print(ridership.mean(axis = 1)) #mean for row

[  1.40000000e+00   2.77920000e+03   3.76880000e+03   3.23560000e+03
   3.50480000e+03   3.41640000e+03   2.55200000e+02   6.00000000e+00
   3.04020000e+03   3.41840000e+03]

pandas DataFrame¶

In [14]:

student = pd.DataFrame({"name":["Arun","Prakash","Balaji","TR","Chidu"],
                       "city":["chennai","chennai","trichy","madurai","madurai"],
                       "age":[25,24,25,26,27]})

In [15]:

print(student)

   age     city     name
0   25  chennai     Arun
1   24  chennai  Prakash
2   25   trichy   Balaji
3   26  madurai       TR
4   27  madurai    Chidu

In [16]:

student = pd.DataFrame(data = student, index=["student1","student2","student3","student4","student5"])
print(student)

          age city name
student1  NaN  NaN  NaN
student2  NaN  NaN  NaN
student3  NaN  NaN  NaN
student4  NaN  NaN  NaN
student5  NaN  NaN  NaN

In [17]:

student = {"name":["Arun","Prakash","Balaji","TR","Chidu"],
                       "city":["chennai","chennai","trichy","madurai","madurai"],
                       "age":[25,24,25,26,27]}

In [18]:

student = pd.DataFrame(data = student, index=["student1","student2","student3","student4","student5"])
print(student)

          age     city     name
student1   25  chennai     Arun
student2   24  chennai  Prakash
student3   25   trichy   Balaji
student4   26  madurai       TR
student5   27  madurai    Chidu

In [19]:

student.loc["student1"]

Out[19]:

age          25
city    chennai
name       Arun
Name: student1, dtype: object

In [20]:

student.iloc[0]

Out[20]:

age          25
city    chennai
name       Arun
Name: student1, dtype: object

In [21]:

student.loc["student1","name"]

Out[21]:

'Arun'

converting pandas dataframe to numpy array¶

In [22]:

print(student.values)

[[25 'chennai' 'Arun']
 [24 'chennai' 'Prakash']
 [25 'trichy' 'Balaji']
 [26 'madurai' 'TR']
 [27 'madurai' 'Chidu']]

In [23]:

print(type(student.values))

<class 'numpy.ndarray'>

pandas practice¶

In [24]:

df_2 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=['A', 'B', 'C'])

In [25]:

print(df_2)

   A  B  C
0  0  1  2
1  3  4  5

In [26]:

df_2 = pd.DataFrame(data = df_2.values, columns=["A","B","C"],index= ["row1","row2"])

In [27]:

print(df_2)

      A  B  C
row1  0  1  2
row2  3  4  5

Calculating correlation between variables (Pearson's r method)¶

Earlier, we calculated the correlation by just checking the relationship between increase or decrease in value of one variable with another variable. Here, we are going to calculate correlation between two variables using pearson's r method.

• Standardize the variable values ( Pandas tip : use var.std(ddof=0) , uncorrected standard deviation)
• Multiply each pair of values and then take average.
• The value of r range in between -1 and +1.
• If the values is positive, then it's positively correlated, if it's negative, then it's negatively correlated.

In [28]:

subway_df = pd.read_csv("nyc_subway_weather.csv")

In [29]:

print(subway_df.head())

   UNIT     DATEn     TIMEn  ENTRIESn   EXITSn  ENTRIESn_hourly  \
0  R003  05-01-11  00:00:00   4388333  2911002              0.0   
1  R003  05-01-11  04:00:00   4388333  2911002              0.0   
2  R003  05-01-11  12:00:00   4388333  2911002              0.0   
3  R003  05-01-11  16:00:00   4388333  2911002              0.0   
4  R003  05-01-11  20:00:00   4388333  2911002              0.0   

   EXITSn_hourly             datetime  hour  day_week     ...       pressurei  \
0            0.0  2011-05-01 00:00:00     0         6     ...           30.22   
1            0.0  2011-05-01 04:00:00     4         6     ...           30.25   
2            0.0  2011-05-01 12:00:00    12         6     ...           30.28   
3            0.0  2011-05-01 16:00:00    16         6     ...           30.26   
4            0.0  2011-05-01 20:00:00    20         6     ...           30.28   

  rain  tempi  wspdi meanprecipi  meanpressurei  meantempi  meanwspdi  \
0    0   55.9    3.5         0.0         30.258      55.98       7.86   
1    0   52.0    3.5         0.0         30.258      55.98       7.86   
2    0   62.1    6.9         0.0         30.258      55.98       7.86   
3    0   57.9   15.0         0.0         30.258      55.98       7.86   
4    0   52.0   10.4         0.0         30.258      55.98       7.86   

   weather_lat  weather_lon  
0    40.700348   -73.887177  
1    40.700348   -73.887177  
2    40.700348   -73.887177  
3    40.700348   -73.887177  
4    40.700348   -73.887177  

[5 rows x 27 columns]

In [30]:

entries = subway_df['ENTRIESn_hourly']
cum_entries = subway_df['ENTRIESn']
rain = subway_df['meanprecipi']
temp = subway_df['meantempi']

In [31]:

print(entries.head(20))

0       0.0
1       0.0
2       0.0
3       0.0
4       0.0
5      15.0
6      19.0
7     488.0
8     490.0
9     231.0
10    235.0
11     74.0
12     20.0
13    975.0
14    267.0
15    277.0
16     83.0
17     24.0
18    532.0
19    454.0
Name: ENTRIESn_hourly, dtype: float64

In [32]:

print(cum_entries.head())

0    4388333
1    4388333
2    4388333
3    4388333
4    4388333
Name: ENTRIESn, dtype: int64

In [33]:

print(rain.head(20))

0     0.00
1     0.00
2     0.00
3     0.00
4     0.00
5     0.00
6     0.00
7     0.00
8     0.00
9     0.00
10    0.00
11    0.00
12    0.00
13    0.00
14    0.00
15    0.00
16    0.01
17    0.01
18    0.01
19    0.01
Name: meanprecipi, dtype: float64

In [34]:

print(temp.head())

0    55.98
1    55.98
2    55.98
3    55.98
4    55.98
Name: meantempi, dtype: float64

Correlation function¶

In [35]:

def correlation(x,y):
    #step 1
    x = (x - x.mean())/x.std(ddof=0)
    y = (y - y.mean())/y.std(ddof = 0)
    #step 2
    r = (x*y).mean()
    return r

In [36]:

print(correlation(entries, rain))
print(correlation(entries, temp))
print(correlation(rain, temp))
print(correlation(entries, cum_entries))

0.03564851577223041
-0.026693348321569912
-0.22903432340833663
0.5858954707662182

Positive correlation example¶

In [37]:

plt.plot(cum_entries,entries)

Out[37]:

[<matplotlib.lines.Line2D at 0x11b22c860>]

Negative correlation example¶

In [38]:

plt.plot(temp,rain)

Out[38]:

[<matplotlib.lines.Line2D at 0x11bbc46d8>]

applymap( ) function¶

In [40]:

#Example 1
df = pd.DataFrame({
        'a': [1, 2, 3],
        'b': [10, 20, 30],
        'c': [5, 10, 15]
    })
    
def add_one(x):
    return x + 1
        
print(df.applymap(add_one))

   a   b   c
0  2  11   6
1  3  21  11
2  4  31  16

In [42]:

#Example 2
grades_df = pd.DataFrame(
    data={'exam1': [43, 81, 78, 75, 89, 70, 91, 65, 98, 87],
          'exam2': [24, 63, 56, 56, 67, 51, 79, 46, 72, 60]},
    index=['Andre', 'Barry', 'Chris', 'Dan', 'Emilio', 
           'Fred', 'Greta', 'Humbert', 'Ivan', 'James']
)
    
def convert_grades(grades):
    '''
    Fill in this function to convert the given DataFrame of numerical
    grades to letter grades. Return a new DataFrame with the converted
    grade.
    
    The conversion rule is:
        90-100 -> A
        80-89  -> B
        70-79  -> C
        60-69  -> D
        0-59   -> F
    '''
    if grades >=90:
        grades = "A"
    elif grades >=80 and grades<90:
        grades = "B"
    elif grades >=70 and grades < 80:
        grades = "C"
    elif grades >=60 and grades <70:
        grades = "D"
    else:
        grades = "F"
    return grades

In [43]:

print(grades_df)

         exam1  exam2
Andre       43     24
Barry       81     63
Chris       78     56
Dan         75     56
Emilio      89     67
Fred        70     51
Greta       91     79
Humbert     65     46
Ivan        98     72
James       87     60

In [44]:

print(grades_df.applymap(convert_grades))

        exam1 exam2
Andre       F     F
Barry       B     D
Chris       C     F
Dan         C     F
Emilio      B     D
Fred        C     F
Greta       A     C
Humbert     D     F
Ivan        A     C
James       B     D

apply( ) function¶

In [45]:

df = pd.DataFrame({
    'a': [4, 5, 3, 1, 2],
    'b': [20, 10, 40, 50, 30],
    'c': [25, 20, 5, 15, 10]
})

In [46]:

print(df)

   a   b   c
0  4  20  25
1  5  10  20
2  3  40   5
3  1  50  15
4  2  30  10

In [51]:

print(df.apply(np.mean))

a     3.0
b    30.0
c    15.0
dtype: float64

In [52]:

print(df.apply(np.max))

a     5
b    50
c    25
dtype: int64

Now let's try to find the second max element in a dataframe using apply funtion.

In [92]:

def second_max(data):
    data_2 = data
    max_data_2 = max(data_2)
    data_2 = data_2.drop(pd.Index(data_2).get_loc(max_data_2))
    return max(data_2)

In [93]:

check =  df["a"]
print(check)
print(max(check))
pos = pd.Index(check).get_loc(max(check))
aa = check.drop(pos)
print(check)
print(aa)

0    4
1    5
2    3
3    1
4    2
Name: a, dtype: int64
5
0    4
1    5
2    3
3    1
4    2
Name: a, dtype: int64
0    4
2    3
3    1
4    2
Name: a, dtype: int64

In [94]:

print(df.apply(second_max))

a     4
b    40
c    20
dtype: int64

Adding a series to a dataframe¶

In [95]:

s = pd.Series([1, 2, 3, 4])
df = pd.DataFrame({
        0: [10, 20, 30, 40],
        1: [50, 60, 70, 80],
        2: [90, 100, 110, 120],
        3: [130, 140, 150, 160]
    })

In [96]:

print(df)

    0   1    2    3
0  10  50   90  130
1  20  60  100  140
2  30  70  110  150
3  40  80  120  160

In [97]:

print(s)

0    1
1    2
2    3
3    4
dtype: int64

In [98]:

print(df+s)

    0   1    2    3
0  11  52   93  134
1  21  62  103  144
2  31  72  113  154
3  41  82  123  164

In [99]:

s = pd.Series([1, 2, 3, 4])
df = pd.DataFrame({0: [10], 1: [20], 2: [30], 3: [40]})

In [102]:

print(df)
print(s)

    0   1   2   3
0  10  20  30  40
0    1
1    2
2    3
3    4
dtype: int64

In [101]:

print(df+s)

    0   1   2   3
0  11  22  33  44

In [105]:

s = pd.Series([1, 2, 3, 4])
df = pd.DataFrame({0: [10, 20, 30, 40]})
print(df)
print(s)

    0
0  10
1  20
2  30
3  40
0    1
1    2
2    3
3    4
dtype: int64

In [104]:

print(df+s)

    0   1   2   3
0  11 NaN NaN NaN
1  21 NaN NaN NaN
2  31 NaN NaN NaN
3  41 NaN NaN NaN

In [106]:

s = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
df = pd.DataFrame({
    'a': [10, 20, 30, 40],
    'b': [50, 60, 70, 80],
    'c': [90, 100, 110, 120],
    'd': [130, 140, 150, 160]
})
print(df)
print(s)

    a   b    c    d
0  10  50   90  130
1  20  60  100  140
2  30  70  110  150
3  40  80  120  160
a    1
b    2
c    3
d    4
dtype: int64

In [107]:

print(df+s)

    a   b    c    d
0  11  52   93  134
1  21  62  103  144
2  31  72  113  154
3  41  82  123  164

groupby( ) function¶

In [108]:

values = np.array([1, 3, 2, 4, 1, 6, 4])
example_df = pd.DataFrame({
    'value': values,
    'even': values % 2 == 0,
    'above_three': values > 3 
}, index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])

In [109]:

print(example_df)

  above_three   even  value
a       False  False      1
b       False  False      3
c       False   True      2
d        True   True      4
e       False  False      1
f        True   True      6
g        True   True      4

In [119]:

print(example_df.groupby("even"))
print(example_df.groupby("even").sum())

<pandas.core.groupby.DataFrameGroupBy object at 0x11bd642e8>
       above_three  value
even                     
False          0.0      5
True           3.0     16

In [116]:

print(example_df.groupby("even").groups)

{False: ['a', 'b', 'e'], True: ['c', 'd', 'f', 'g']}

In [117]:

# groupby multiple columns 

grouped_data = example_df.groupby(['even', 'above_three'])
print(grouped_data.groups)

{(True, False): ['c'], (False, False): ['a', 'b', 'e'], (True, True): ['d', 'f', 'g']}

Merging multiple pandas Dataframes¶

In [131]:

subway_df = pd.DataFrame({
    'UNIT': ['R003', 'R003', 'R003', 'R003', 'R003', 'R004', 'R004', 'R004',
             'R004', 'R004'],
    'DATEn': ['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',
              '05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11'],
    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'ENTRIESn': [ 4388333,  4388348,  4389885,  4391507,  4393043, 14656120,
                 14656174, 14660126, 14664247, 14668301],
    'EXITSn': [ 2911002,  2911036,  2912127,  2913223,  2914284, 14451774,
               14451851, 14454734, 14457780, 14460818],
    'latitude': [ 40.689945,  40.689945,  40.689945,  40.689945,  40.689945,
                  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ],
    'longitude': [-73.872564, -73.872564, -73.872564, -73.872564, -73.872564,
                  -73.867135, -73.867135, -73.867135, -73.867135, -73.867135]
})

weather_df = pd.DataFrame({
    'DATEn': ['05-01-11', '05-01-11', '05-02-11', '05-02-11', '05-03-11',
              '05-03-11', '05-04-11', '05-04-11', '05-05-11', '05-05-11'],
    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'latitude': [ 40.689945,  40.69132 ,  40.689945,  40.69132 ,  40.689945,
                  40.69132 ,  40.689945,  40.69132 ,  40.689945,  40.69132 ],
    'longitude': [-73.872564, -73.867135, -73.872564, -73.867135, -73.872564,
                  -73.867135, -73.872564, -73.867135, -73.872564, -73.867135],
    'pressurei': [ 30.24,  30.24,  30.32,  30.32,  30.14,  30.14,  29.98,  29.98,
                   30.01,  30.01],
    'fog': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'rain': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'tempi': [ 52. ,  52. ,  48.9,  48.9,  54. ,  54. ,  57.2,  57.2,  48.9,  48.9],
    'wspdi': [  8.1,   8.1,   6.9,   6.9,   3.5,   3.5,  15. ,  15. ,  15. ,  15. ]
})

In [132]:

subway_df

Out[132]:

	DATEn	ENTRIESn	EXITSn	UNIT	hour	latitude	longitude
0	05-01-11	4388333	2911002	R003	0	40.689945	-73.872564
1	05-02-11	4388348	2911036	R003	0	40.689945	-73.872564
2	05-03-11	4389885	2912127	R003	0	40.689945	-73.872564
3	05-04-11	4391507	2913223	R003	0	40.689945	-73.872564
4	05-05-11	4393043	2914284	R003	0	40.689945	-73.872564
5	05-01-11	14656120	14451774	R004	0	40.691320	-73.867135
6	05-02-11	14656174	14451851	R004	0	40.691320	-73.867135
7	05-03-11	14660126	14454734	R004	0	40.691320	-73.867135
8	05-04-11	14664247	14457780	R004	0	40.691320	-73.867135
9	05-05-11	14668301	14460818	R004	0	40.691320	-73.867135

In [133]:

weather_df

Out[133]:

	DATEn	fog	hour	latitude	longitude	pressurei	rain	tempi	wspdi
0	05-01-11	0	0	40.689945	-73.872564	30.24	0	52.0	8.1
1	05-01-11	0	0	40.691320	-73.867135	30.24	0	52.0	8.1
2	05-02-11	0	0	40.689945	-73.872564	30.32	0	48.9	6.9
3	05-02-11	0	0	40.691320	-73.867135	30.32	0	48.9	6.9
4	05-03-11	0	0	40.689945	-73.872564	30.14	0	54.0	3.5
5	05-03-11	0	0	40.691320	-73.867135	30.14	0	54.0	3.5
6	05-04-11	0	0	40.689945	-73.872564	29.98	0	57.2	15.0
7	05-04-11	0	0	40.691320	-73.867135	29.98	0	57.2	15.0
8	05-05-11	0	0	40.689945	-73.872564	30.01	0	48.9	15.0
9	05-05-11	0	0	40.691320	-73.867135	30.01	0	48.9	15.0

In [134]:

subway_df.merge(weather_df, on = ["DATEn","hour","latitude","longitude"],how = "inner")

Out[134]:

	DATEn	ENTRIESn	EXITSn	UNIT	hour	latitude	longitude	fog	pressurei	rain	tempi	wspdi
0	05-01-11	4388333	2911002	R003	0	40.689945	-73.872564	0	30.24	0	52.0	8.1
1	05-02-11	4388348	2911036	R003	0	40.689945	-73.872564	0	30.32	0	48.9	6.9
2	05-03-11	4389885	2912127	R003	0	40.689945	-73.872564	0	30.14	0	54.0	3.5
3	05-04-11	4391507	2913223	R003	0	40.689945	-73.872564	0	29.98	0	57.2	15.0
4	05-05-11	4393043	2914284	R003	0	40.689945	-73.872564	0	30.01	0	48.9	15.0
5	05-01-11	14656120	14451774	R004	0	40.691320	-73.867135	0	30.24	0	52.0	8.1
6	05-02-11	14656174	14451851	R004	0	40.691320	-73.867135	0	30.32	0	48.9	6.9
7	05-03-11	14660126	14454734	R004	0	40.691320	-73.867135	0	30.14	0	54.0	3.5
8	05-04-11	14664247	14457780	R004	0	40.691320	-73.867135	0	29.98	0	57.2	15.0
9	05-05-11	14668301	14460818	R004	0	40.691320	-73.867135	0	30.01	0	48.9	15.0

Plotting¶

In [136]:

subway_df = pd.read_csv("nyc_subway_weather.csv")
subway_df.head()

Out[136]:

	UNIT	DATEn	TIMEn	ENTRIESn	EXITSn	ENTRIESn_hourly	EXITSn_hourly	datetime	hour	day_week	...	pressurei	rain	tempi	wspdi	meanprecipi	meanpressurei	meantempi	meanwspdi	weather_lat	weather_lon
0	R003	05-01-11	00:00:00	4388333	2911002	0.0	0.0	2011-05-01 00:00:00	0	6	...	30.22	0	55.9	3.5	0.0	30.258	55.98	7.86	40.700348	-73.887177
1	R003	05-01-11	04:00:00	4388333	2911002	0.0	0.0	2011-05-01 04:00:00	4	6	...	30.25	0	52.0	3.5	0.0	30.258	55.98	7.86	40.700348	-73.887177
2	R003	05-01-11	12:00:00	4388333	2911002	0.0	0.0	2011-05-01 12:00:00	12	6	...	30.28	0	62.1	6.9	0.0	30.258	55.98	7.86	40.700348	-73.887177
3	R003	05-01-11	16:00:00	4388333	2911002	0.0	0.0	2011-05-01 16:00:00	16	6	...	30.26	0	57.9	15.0	0.0	30.258	55.98	7.86	40.700348	-73.887177
4	R003	05-01-11	20:00:00	4388333	2911002	0.0	0.0	2011-05-01 20:00:00	20	6	...	30.28	0	52.0	10.4	0.0	30.258	55.98	7.86	40.700348	-73.887177

5 rows × 27 columns

In [139]:

data_by_location = subway_df.groupby(["latitude","longitude"],as_index=False).mean()

In [140]:

data_by_location.head()

Out[140]:

	latitude	longitude	ENTRIESn	EXITSn	ENTRIESn_hourly	EXITSn_hourly	hour	day_week	weekday	fog	...	pressurei	rain	tempi	wspdi	meanprecipi	meanpressurei	meantempi	meanwspdi	weather_lat	weather_lon
0	40.576152	-73.975925	9.659049e+06	8.641132e+06	403.896175	325.956284	10.032787	2.907104	0.715847	0.010929	...	29.972568	0.229508	63.383607	5.553005	0.006284	29.972568	63.383607	5.553005	40.603489	-73.958763
1	40.576298	-73.968523	8.306897e+06	6.646823e+06	526.697297	419.562162	9.989189	2.951351	0.708108	0.010811	...	29.973297	0.227027	63.375135	5.517838	0.006216	29.973297	63.375135	5.517838	40.603489	-73.958763
2	40.577961	-73.961806	4.552910e+07	4.612408e+07	1950.295699	1930.483871	10.000000	2.935484	0.709677	0.010753	...	29.973118	0.225806	63.394086	5.531720	0.006183	29.973118	63.394086	5.531720	40.603489	-73.958763
3	40.589547	-73.974295	7.268214e+06	7.961334e+06	485.382353	362.941176	10.164706	2.905882	0.705882	0.011765	...	29.971176	0.200000	63.650588	5.630588	0.006118	29.971176	63.650588	5.630588	40.603489	-73.958763
4	40.590867	-73.797011	6.477945e+06	5.994957e+06	500.725610	374.628049	10.097561	2.951220	0.719512	0.024390	...	29.981098	0.195122	61.721341	9.945122	0.002744	29.981098	61.721341	9.945122	40.660004	-73.844849

5 rows × 21 columns

In [142]:

plt.scatter(data_by_location["latitude"],data_by_location["longitude"], s=data_by_location["ENTRIESn_hourly"])

Out[142]:

<matplotlib.collections.PathCollection at 0x12410de48>

In [145]:

scaled_entries = (data_by_location["ENTRIESn_hourly"]/data_by_location["ENTRIESn_hourly"].std())

In [146]:

plt.scatter(data_by_location["latitude"],data_by_location["longitude"], s=scaled_entries)

Out[146]:

<matplotlib.collections.PathCollection at 0x129498898>