
from google.colab import drive
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression 
import statsmodels.api as sm
from scipy.stats import ttest_ind
import seaborn as sns
drive.mount('content')

/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm

Mounted at content


beyonce = pd.read_csv("content/MyDrive/python_scratch/spotify_data/beyonce.csv")


print(beyonce)

     Unnamed: 0 artist_name  ... mode_name  key_mode
0             1     Beyoncé  ...     major   B major
1             2     Beyoncé  ...     minor  C# minor
2             3     Beyoncé  ...     minor   F minor
3             4     Beyoncé  ...     major   C major
4             5     Beyoncé  ...     minor   A minor
..          ...         ...  ...       ...       ...
433         434     Beyoncé  ...     major  F# major
434         435     Beyoncé  ...     minor   F minor
435         436     Beyoncé  ...     minor   B minor
436         437     Beyoncé  ...     major   F major
437         438     Beyoncé  ...     major   E major

[438 rows x 40 columns]


beyonce.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 438 entries, 0 to 437
Data columns (total 40 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Unnamed: 0                    438 non-null    int64  
 1   artist_name                   438 non-null    object 
 2   artist_id                     438 non-null    object 
 3   album_id                      438 non-null    object 
 4   album_type                    438 non-null    object 
 5   album_images                  438 non-null    object 
 6   album_release_date            438 non-null    object 
 7   album_release_year            438 non-null    int64  
 8   album_release_date_precision  438 non-null    object 
 9   danceability                  438 non-null    float64
 10  energy                        438 non-null    float64
 11  key                           438 non-null    int64  
 12  loudness                      438 non-null    float64
 13  mode                          438 non-null    int64  
 14  speechiness                   438 non-null    float64
 15  acousticness                  438 non-null    float64
 16  instrumentalness              438 non-null    float64
 17  liveness                      438 non-null    float64
 18  valence                       438 non-null    float64
 19  tempo                         438 non-null    float64
 20  track_id                      438 non-null    object 
 21  analysis_url                  438 non-null    object 
 22  time_signature                438 non-null    int64  
 23  artists                       438 non-null    object 
 24  available_markets             438 non-null    object 
 25  disc_number                   438 non-null    int64  
 26  duration_ms                   438 non-null    int64  
 27  explicit                      438 non-null    bool   
 28  track_href                    438 non-null    object 
 29  is_local                      438 non-null    bool   
 30  track_name                    438 non-null    object 
 31  track_preview_url             436 non-null    object 
 32  track_number                  438 non-null    int64  
 33  type                          438 non-null    object 
 34  track_uri                     438 non-null    object 
 35  external_urls.spotify         438 non-null    object 
 36  album_name                    438 non-null    object 
 37  key_name                      438 non-null    object 
 38  mode_name                     438 non-null    object 
 39  key_mode                      438 non-null    object 
dtypes: bool(2), float64(9), int64(8), object(21)
memory usage: 131.0+ KB


## shows the last 5 rows:
beyonce.tail()


#shows the first 3 rows:
beyonce.head(3)


beyonce.columns

Index(['Unnamed: 0', 'artist_name', 'artist_id', 'album_id', 'album_type',
       'album_images', 'album_release_date', 'album_release_year',
       'album_release_date_precision', 'danceability', 'energy', 'key',
       'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness',
       'liveness', 'valence', 'tempo', 'track_id', 'analysis_url',
       'time_signature', 'artists', 'available_markets', 'disc_number',
       'duration_ms', 'explicit', 'track_href', 'is_local', 'track_name',
       'track_preview_url', 'track_number', 'type', 'track_uri',
       'external_urls.spotify', 'album_name', 'key_name', 'mode_name',
       'key_mode'],
      dtype='object')


beyonce.dtypes

Unnamed: 0                        int64
artist_name                      object
artist_id                        object
album_id                         object
album_type                       object
album_images                     object
album_release_date               object
album_release_year                int64
album_release_date_precision     object
danceability                    float64
energy                          float64
key                               int64
loudness                        float64
mode                              int64
speechiness                     float64
acousticness                    float64
instrumentalness                float64
liveness                        float64
valence                         float64
tempo                           float64
track_id                         object
analysis_url                     object
time_signature                    int64
artists                          object
available_markets                object
disc_number                       int64
duration_ms                       int64
explicit                           bool
track_href                       object
is_local                           bool
track_name                       object
track_preview_url                object
track_number                      int64
type                             object
track_uri                        object
external_urls.spotify            object
album_name                       object
key_name                         object
mode_name                        object
key_mode                         object
dtype: object


beyonce.shape

(438, 40)


###finds the fourth row:

beyonce.iloc[3]

Unnamed: 0                                                                      4
artist_name                                                               Beyoncé
artist_id                                                  6vWDO969PvNqNYHIOW5v0m
album_id                                                   2UJwKSBUz6rtW4QLK74kQu
album_type                                                                  album
album_images                    list(height = c(640, 300, 64), url = c("https:...
album_release_date                                                     2014-11-24
album_release_year                                                           2014
album_release_date_precision                                                  day
danceability                                                                0.875
energy                                                                      0.434
key                                                                             0
loudness                                                                   -7.683
mode                                                                            1
speechiness                                                                 0.091
acousticness                                                                0.242
instrumentalness                                                          0.00146
liveness                                                                    0.234
valence                                                                     0.777
tempo                                                                     120.065
track_id                                                   6wwrYruEgWlowPDZMq5116
analysis_url                    https://api.spotify.com/v1/audio-analysis/6wwr...
time_signature                                                                  4
artists                         list(href = "https://api.spotify.com/v1/artist...
available_markets               c("AD", "AR", "AT", "AU", "BE", "BG", "BO", "B...
disc_number                                                                     1
duration_ms                                                                309720
explicit                                                                    False
track_href                      https://api.spotify.com/v1/tracks/6wwrYruEgWlo...
is_local                                                                    False
track_name                                                                   Blow
track_preview_url               https://p.scdn.co/mp3-preview/7939ef46f6f8992a...
track_number                                                                    4
type                                                                        track
track_uri                                    spotify:track:6wwrYruEgWlowPDZMq5116
external_urls.spotify           https://open.spotify.com/track/6wwrYruEgWlowPD...
album_name                                             BEYONCÉ [Platinum Edition]
key_name                                                                        C
mode_name                                                                   major
key_mode                                                                  C major
Name: 3, dtype: object


## or a range:

beyonce.iloc[-1]

Unnamed: 0                                                                    438
artist_name                                                               Beyoncé
artist_id                                                  6vWDO969PvNqNYHIOW5v0m
album_id                                                   25hVFAxTlDvXbx2X2QkUkE
album_type                                                                  album
album_images                    list(height = c(640, 300, 64), url = c("https:...
album_release_date                                                           2003
album_release_year                                                           2003
album_release_date_precision                                                 year
danceability                                                                0.515
energy                                                                      0.705
key                                                                             4
loudness                                                                   -7.924
mode                                                                            1
speechiness                                                                  0.37
acousticness                                                                 0.24
instrumentalness                                                                0
liveness                                                                    0.182
valence                                                                     0.198
tempo                                                                      89.861
track_id                                                   6IeECiLESgmQDy5IKt5tiA
analysis_url                    https://api.spotify.com/v1/audio-analysis/6IeE...
time_signature                                                                  4
artists                         list(href = c("https://api.spotify.com/v1/arti...
available_markets                                                    character(0)
disc_number                                                                     1
duration_ms                                                                517120
explicit                                                                    False
track_href                      https://api.spotify.com/v1/tracks/6IeECiLESgmQ...
is_local                                                                    False
track_name                                                     '03 Bonnie & Clyde
track_preview_url                                                             NaN
track_number                                                                   16
type                                                                        track
track_uri                                    spotify:track:6IeECiLESgmQDy5IKt5tiA
external_urls.spotify           https://open.spotify.com/track/6IeECiLESgmQDy5...
album_name                           Dangerously In Love (Alben für die Ewigkeit)
key_name                                                                        E
mode_name                                                                   major
key_mode                                                                  E major
Name: 437, dtype: object


beyonce.loc[1:3, 'tempo']

1    122.822
2    140.030
3    120.065
Name: tempo, dtype: float64


beyonce_tempi = beyonce['tempo']
for i in beyonce_tempi:
  if i < 120:
    print("slow")
  else:
    print("fast")


###or just find fast pieces:
tempo = beyonce['tempo']  
fast = beyonce[tempo > 160]
print(fast)


## the mean "acousticness" in Beyonce.
beyonce["acousticness"].mean()

0.18018508515981732


## the median "acousticness" in Beyonce.
beyonce["acousticness"].median()

0.0947


beyonce[["tempo", "danceability", "acousticness"]].describe()


## are Beyonce's "explicit" songs faster?
beyonce[["tempo", "explicit"]].groupby("explicit").mean()


## do they differ in other ways?
beyonce.groupby("explicit").mean()


### does meter matter?
beyonce.groupby(["explicit", "time_signature"])["tempo"].mean()

explicit  time_signature
False     1                 125.738000
          3                 113.264684
          4                 118.316360
          5                  79.544625
True      3                 140.415000
          4                 130.914375
Name: tempo, dtype: float64


# what are these counts, though?
beyonce.groupby(["explicit", "time_signature"])["tempo"].count()

explicit  time_signature
False     1                   5
          3                  38
          4                 378
          5                   8
True      3                   1
          4                   8
Name: tempo, dtype: int64


## standard deviation 
beyonce["acousticness"].std()

0.21086927911388673


tempo = beyonce['tempo']  
danceability = beyonce['danceability']  
tempo = tempo.values.reshape(-1, 1)  # values converts it into a numpy array
danceability = danceability.values.reshape(-1, 1)
linear_regressor = LinearRegression()  # create object for the class
estimate = linear_regressor.fit(tempo, danceability)  # perform linear regression
danceability_pred = linear_regressor.predict(tempo)  # make predictions


plt.scatter(tempo, danceability)
plt.plot(tempo, danceability_pred, color='red')
plt.show()


### using the stats toolkit (sm) we can get specifics of the model.
tempo_2 = sm.add_constant(tempo)
est = sm.OLS(danceability, tempo_2)
est2 = est.fit()
print(est2.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.001
Model:                            OLS   Adj. R-squared:                 -0.001
Method:                 Least Squares   F-statistic:                    0.3908
Date:                Mon, 29 Mar 2021   Prob (F-statistic):              0.532
Time:                        19:01:01   Log-Likelihood:                 158.80
No. Observations:                 438   AIC:                            -313.6
Df Residuals:                     436   BIC:                            -305.4
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.5613      0.031     18.313      0.000       0.501       0.622
x1             0.0002      0.000      0.625      0.532      -0.000       0.001
==============================================================================
Omnibus:                       18.672   Durbin-Watson:                   1.261
Prob(Omnibus):                  0.000   Jarque-Bera (JB):                9.201
Skew:                          -0.130   Prob(JB):                       0.0100
Kurtosis:                       2.339   Cond. No.                         463.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


year = beyonce['album_release_year'].values.reshape(-1,1)
danceability = beyonce['danceability'].values.reshape(-1,1)
linear_regressor = LinearRegression()  # create object for the class
estimate = linear_regressor.fit(year, danceability)  # perform linear regression
danceability_pred = linear_regressor.predict(year)  # make predictions
plt.scatter(year, danceability)
plt.plot(year, danceability_pred, color='red')
plt.show()


### using the stats toolkit (sm) we can get specifics of the model.
tempo_2 = sm.add_constant(tempo)
est = sm.OLS(year, tempo_2)
est2 = est.fit()
print(est2.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.004
Model:                            OLS   Adj. R-squared:                  0.001
Method:                 Least Squares   F-statistic:                     1.600
Date:                Mon, 29 Mar 2021   Prob (F-statistic):              0.207
Time:                        19:02:50   Log-Likelihood:                -1094.0
No. Observations:                 438   AIC:                             2192.
Df Residuals:                     436   BIC:                             2200.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const       2007.4426      0.535   3750.100      0.000    2006.390    2008.495
x1             0.0056      0.004      1.265      0.207      -0.003       0.014
==============================================================================
Omnibus:                        9.957   Durbin-Watson:                   0.011
Prob(Omnibus):                  0.007   Jarque-Bera (JB):                9.879
Skew:                           0.336   Prob(JB):                      0.00716
Kurtosis:                       2.699   Cond. No.                         463.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Ttest_indResult(statistic=array([-0.57972078]), pvalue=array([0.56223576]))


taylor = pd.read_csv("content/MyDrive/python_scratch/spotify_data/taylor.csv")


beyonce_tempo = beyonce['tempo'].values.reshape(-1,1)
taylor_tempo = taylor['tempo'].values.reshape(-1,1)


tset, pval = ttest_ind(beyonce_tempo, taylor_tempo)
print(f"the p-value is:{pval}, tset")
if pval < 0.05:    # alpha value is 0.05 or 5%
   print("We are rejecting the null hypothesis. Hooray")
else:
  print("We are accepting null hypothesis that the distributions are not significantly different.")

the p-value is:[0.56223576], tset
We are accepting null hypothesis that the distributions are not significantly different.


tempi = list(zip(taylor_tempo, beyonce_tempo))
df = pd.DataFrame(tempi, columns = ['Taylor', 'Beyonce'])


print(df)

                   Taylor    Beyonce
0               [160.015]  [129.935]
1    [159.07299999999998]  [122.822]
2     [82.98899999999999]   [140.03]
3               [135.917]  [120.065]
4                [95.045]   [111.58]
..                    ...        ...
433   [96.03399999999999]   [99.947]
434  [108.76700000000001]  [133.173]
435  [100.02799999999999]   [93.615]
436              [61.867]  [173.954]
437   [95.48700000000001]   [89.861]

[438 rows x 2 columns]


sns.distplot(df[['Taylor']], hist=False, rug=True)
sns.distplot(df[['Beyonce']], hist=False, rug=True)

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2056: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2056: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.
  warnings.warn(msg, FutureWarning)

<matplotlib.axes._subplots.AxesSubplot at 0x7f562f9dce90>


### your code here.

class Compare_Data:

  def __init__(self, source1, source2):

    self.file_1 = pd.read_csv(source1)
    self.file_2 = pd.read_csv(source2)

  def plot_tempi(self):
    tempo1 = self.file_1["tempo"].values.reshape(-1,1)
    tempo2 = self.file_2["tempo"].values.reshape(-1,1)
    sns.distplot(df[[tempo1]], hist=False, rug=True)
    sns.distplot(df[[tempo2]], hist=False, rug=True)

 # def compare_danceability() :


comparison = Compare_Data("content/MyDrive/python_scratch/spotify_data/taylor.csv", "content/MyDrive/python_scratch/spotify_data/beyonce.csv")


comparison.plot_tempi()

1[[160.015]
 [159.073]
 [ 82.989]
 [135.917]
 [ 95.045]
 [128.07 ]
 [ 74.957]
 [ 92.027]
 [172.054]
 [110.01 ]
 [160.024]
 [120.085]
 [163.96 ]
 [163.954]
 [ 94.922]
 [ 93.061]
 [139.997]
 [126.068]
 [124.985]
 [157.043]
 [ 99.959]
 [178.013]
 [104.009]
 [ 77.318]
 [117.97 ]
 [163.886]
 [119.997]
 [ 74.957]
 [ 95.122]
 [121.034]
 [114.977]
 [129.962]
 [162.012]
 [124.894]
 [109.995]
 [171.791]
 [116.992]
 [100.163]
 [ 81.963]
 [ 96.006]
 [159.837]
 [ 92.008]
 [ 79.926]
 [114.984]
 [143.95 ]
 [117.935]
 [ 96.005]
 [ 73.849]
 [143.944]
 [ 80.007]
 [ 79.025]
 [146.147]
 [139.908]
 [128.057]
 [116.001]
 [118.986]
 [184.014]
 [ 92.673]
 [143.936]
 [126.014]
 [159.965]
 [ 67.303]
 [160.015]
 [172.909]
 [159.073]
 [ 98.753]
 [ 82.989]
 [ 86.424]
 [135.917]
 [ 85.319]
 [ 95.045]
 [ 77.411]
 [128.07 ]
 [123.703]
 [ 74.957]
 [ 76.531]
 [ 92.027]
 [129.971]
 [172.054]
 [ 83.407]
 [110.01 ]
 [129.863]
 [160.024]
 [100.283]
 [120.085]
 [130.302]
 [163.96 ]
 [ 68.253]
 [163.954]
 [130.315]
 [ 94.922]
 [ 69.091]
 [117.023]
 [ 96.009]
 [ 95.021]
 [ 91.993]
 [ 96.969]
 [160.02 ]
 [118.027]
 [170.157]
 [140.06 ]
 [119.982]
 [ 71.981]
 [159.959]
 [103.981]
 [117.023]
 [ 96.009]
 [ 95.021]
 [ 91.993]
 [ 96.969]
 [160.02 ]
 [118.027]
 [170.157]
 [140.06 ]
 [119.982]
 [ 71.981]
 [159.959]
 [103.981]
 [117.015]
 [191.963]
 [ 94.997]
 [ 92.012]
 [ 96.995]
 [159.967]
 [117.976]
 [170.057]
 [140.046]
 [119.98 ]
 [144.057]
 [160.052]
 [103.989]
 [ 77.502]
 [116.992]
 [100.356]
 [ 95.997]
 [ 88.155]
 [ 94.933]
 [ 93.705]
 [ 92.008]
 [105.468]
 [ 96.97 ]
 [ 87.915]
 [160.078]
 [ 79.281]
 [118.035]
 [ 81.512]
 [170.216]
 [ 86.883]
 [139.997]
 [ 79.214]
 [119.997]
 [ 78.761]
 [143.95 ]
 [113.854]
 [159.965]
 [ 99.561]
 [103.97 ]
 [117.012]
 [ 96.031]
 [ 94.992]
 [ 92.008]
 [ 97.031]
 [160.041]
 [118.026]
 [170.141]
 [140.035]
 [119.987]
 [143.919]
 [ 79.999]
 [103.975]
 [129.943]
 [124.962]
 [110.001]
 [154.228]
 [ 93.052]
 [103.986]
 [145.901]
 [ 86.035]
 [ 99.989]
 [ 93.986]
 [157.001]
 [129.975]
 [117.942]
 [ 80.042]
 [126.003]
 [ 78.748]
 [129.979]
 [124.988]
 [110.007]
 [ 77.02 ]
 [ 93.01 ]
 [104.001]
 [146.084]
 [ 86.014]
 [ 99.993]
 [ 94.016]
 [157.053]
 [129.992]
 [117.972]
 [ 80.034]
 [126.022]
 [ 79.002]
 [129.986]
 [124.989]
 [110.04 ]
 [ 77.001]
 [ 93.014]
 [103.989]
 [146.034]
 [ 86.005]
 [100.002]
 [ 94.015]
 [157.02 ]
 [129.965]
 [117.99 ]
 [ 80.048]
 [126.025]
 [ 78.966]
 [ 88.084]
 [129.968]
 [ 92.483]
 [124.978]
 [173.317]
 [109.995]
 [ 87.576]
 [ 77.019]
 [123.849]
 [ 93.06 ]
 [133.933]
 [103.987]
 [ 95.101]
 [145.865]
 [ 68.151]
 [ 85.984]
 [ 77.449]
 [ 99.981]
 [ 94.766]
 [ 94.069]
 [ 87.227]
 [157.043]
 [ 84.382]
 [129.987]
 [ 78.286]
 [117.937]
 [101.873]
 [ 80.016]
 [134.89 ]
 [126.018]
 [ 95.371]
 [ 79.025]
 [121.07 ]
 [114.987]
 [141.893]
 [118.975]
 [119.386]
 [163.974]
 [139.898]
 [124.91 ]
 [163.893]
 [145.821]
 [134.05 ]
 [162.088]
 [ 89.038]
 [204.125]
 [121.045]
 [114.987]
 [141.971]
 [118.981]
 [119.512]
 [163.973]
 [140.012]
 [124.995]
 [ 82.003]
 [146.023]
 [134.024]
 [162.185]
 [159.012]
 [102.016]
 [114.976]
 [121.013]
 [139.756]
 [ 82.008]
 [ 78.323]
 [142.043]
 [145.921]
 [118.879]
 [158.027]
 [ 81.995]
 [102.11 ]
 [ 97.406]
 [119.395]
 [ 81.988]
 [162.288]
 [101.979]
 [114.981]
 [120.969]
 [139.813]
 [163.752]
 [ 77.769]
 [142.057]
 [145.954]
 [118.876]
 [ 79.517]
 [109.025]
 [104.46 ]
 [ 97.397]
 [119.518]
 [163.678]
 [162.157]
 [101.939]
 [ 66.869]
 [121.061]
 [ 64.792]
 [114.998]
 [128.394]
 [141.893]
 [ 85.222]
 [118.959]
 [ 83.656]
 [119.367]
 [ 85.001]
 [163.98 ]
 [ 95.847]
 [139.907]
 [143.429]
 [124.899]
 [ 51.351]
 [163.9  ]
 [104.978]
 [145.87 ]
 [146.991]
 [134.032]
 [149.646]
 [162.145]
 [ 84.599]
 [ 72.619]
 [ 74.299]
 [204.104]
 [121.042]
 [114.975]
 [141.887]
 [118.958]
 [119.443]
 [163.947]
 [139.973]
 [124.897]
 [ 81.917]
 [145.871]
 [134.008]
 [162.127]
 [158.867]
 [102.013]
 [100.019]
 [ 95.431]
 [119.054]
 [116.001]
 [ 92.354]
 [129.967]
 [148.305]
 [ 99.978]
 [134.059]
 [160.92 ]
 [128.048]
 [126.189]
 [ 95.996]
 [177.822]
 [ 99.942]
 [168.137]
 [ 96.019]
 [105.583]
 [143.997]
 [105.995]
 [200.056]
 [ 95.349]
 [119.056]
 [115.994]
 [ 92.71 ]
 [129.98 ]
 [147.913]
 [ 99.989]
 [133.956]
 [160.92 ]
 [128.007]
 [126.074]
 [ 96.018]
 [177.865]
 [ 99.947]
 [168.077]
 [127.997]
 [ 99.972]
 [ 95.485]
 [118.984]
 [115.994]
 [ 92.702]
 [129.964]
 [ 73.975]
 [ 99.96 ]
 [133.959]
 [160.927]
 [128.042]
 [126.084]
 [ 96.013]
 [100.047]
 [ 95.469]
 [119.06 ]
 [116.048]
 [ 92.613]
 [130.009]
 [ 73.989]
 [100.024]
 [134.074]
 [160.901]
 [128.034]
 [126.032]
 [ 96.015]
 [ 79.985]
 [199.965]
 [120.02 ]
 [143.952]
 [ 85.953]
 [ 81.921]
 [100.029]
 [ 95.407]
 [119.004]
 [115.972]
 [185.776]
 [129.987]
 [147.988]
 [ 99.99 ]
 [133.927]
 [160.884]
 [128.046]
 [126.075]
 [ 96.034]
 [108.767]
 [100.028]
 [ 61.867]
 [ 95.487]
 [ 90.743]
 [118.984]
 [ 47.607]
 [116.01 ]
 [103.247]
 [ 92.598]
 [126.254]
 [129.966]
 [ 99.358]
 [147.988]
 [ 99.948]
 [100.007]
 [ 62.327]
 [133.939]
 [111.601]
 [160.989]
 [ 67.722]
 [128.052]
 [ 84.469]
 [126.083]
 [ 78.855]
 [ 96.   ]
 [100.036]
 [ 95.452]
 [118.994]
 [116.009]
 [ 92.166]
 [129.973]
 [147.96 ]
 [100.074]
 [133.932]
 [160.933]
 [128.024]
 [126.093]
 [ 95.953]
 [152.175]
 [105.587]
 [ 99.966]
 [115.012]
 [175.662]
 [112.979]
 [146.172]
 [131.646]
 [167.974]
 [150.802]
 [177.874]
 [143.983]
 [ 96.052]
 [156.035]
 [100.021]
 [151.953]
 [105.494]
 [ 99.968]
 [115.005]
 [175.597]
 [113.013]
 [146.132]
 [131.654]
 [167.914]
 [ 73.209]
 [ 89.027]
 [143.936]
 [ 96.023]
 [156.145]
 [199.9  ]
 [ 75.994]
 [105.482]
 [100.064]
 [115.012]
 [ 87.958]
 [113.021]
 [146.059]
 [131.729]
 [168.087]
 [151.048]
 [177.997]
 [144.013]
 [ 96.001]
 [155.972]
 [171.246]
 [ 76.   ]
 [ 60.266]
 [105.607]
 [ 84.758]
 [ 99.962]
 [ 75.683]
 [114.982]
 [138.159]
 [175.974]
 [ 94.383]
 [113.002]
 [ 67.906]
 [146.118]
 [ 99.845]
 [131.607]
 [118.747]
 [167.935]
 [128.144]
 [150.721]
 [ 83.734]
 [ 89.128]
 [108.258]
 [143.94 ]
 [ 77.762]
 [ 96.02 ]
 [ 81.209]
 [156.088]
 [ 80.865]
 [199.909]]
2[[129.935]
 [122.822]
 [140.03 ]
 [120.065]
 [111.58 ]
 [185.571]
 [133.97 ]
 [140.415]
 [103.009]
 [170.085]
 [140.553]
 [ 80.334]
 [102.601]
 [ 90.567]
 [136.024]
 [136.801]
 [139.748]
 [ 98.011]
 [120.024]
 [195.956]
 [129.935]
 [122.278]
 [140.006]
 [120.068]
 [111.58 ]
 [ 87.953]
 [133.929]
 [140.419]
 [103.012]
 [170.056]
 [136.832]
 [ 80.334]
 [102.601]
 [ 90.567]
 [136.024]
 [136.825]
 [139.672]
 [ 98.028]
 [120.034]
 [124.979]
 [128.015]
 [ 83.703]
 [119.496]
 [127.993]
 [127.995]
 [117.913]
 [ 63.345]
 [ 86.934]
 [167.963]
 [ 99.099]
 [160.979]
 [ 72.032]
 [150.006]
 [ 94.1  ]
 [167.349]
 [117.999]
 [110.992]
 [127.086]
 [116.01 ]
 [124.04 ]
 [110.005]
 [127.003]
 [128.02 ]
 [129.022]
 [ 63.345]
 [ 86.934]
 [167.963]
 [ 99.099]
 [160.979]
 [ 72.032]
 [150.006]
 [ 94.1  ]
 [167.349]
 [117.999]
 [110.992]
 [127.086]
 [ 63.345]
 [ 86.934]
 [167.963]
 [ 99.099]
 [160.979]
 [ 72.032]
 [150.006]
 [ 94.1  ]
 [167.349]
 [117.999]
 [110.992]
 [127.086]
 [ 94.1  ]
 [160.979]
 [124.04 ]
 [167.349]
 [167.963]
 [110.005]
 [ 86.934]
 [ 72.032]
 [ 63.345]
 [117.999]
 [127.086]
 [ 99.099]
 [150.006]
 [110.992]
 [148.045]
 [ 99.13 ]
 [ 99.946]
 [ 99.027]
 [ 96.618]
 [ 94.982]
 [104.838]
 [169.502]
 [100.105]
 [146.248]
 [135.995]
 [ 83.132]
 [129.117]
 [ 91.13 ]
 [144.848]
 [ 88.515]
 [ 83.213]
 [103.293]
 [ 84.937]
 [137.164]
 [ 75.195]
 [124.061]
 [ 97.083]
 [143.024]
 [155.075]
 [125.999]
 [128.005]
 [131.997]
 [128.008]
 [168.247]
 [122.988]
 [126.995]
 [ 78.781]
 [ 90.007]
 [ 79.983]
 [ 78.412]
 [ 82.149]
 [104.957]
 [ 95.068]
 [ 64.639]
 [115.036]
 [116.966]
 [193.437]
 [135.771]
 [146.092]
 [121.949]
 [ 84.769]
 [ 97.515]
 [ 79.454]
 [ 86.961]
 [116.966]
 [ 90.007]
 [ 79.983]
 [ 78.412]
 [ 82.149]
 [104.957]
 [ 95.068]
 [109.859]
 [115.036]
 [193.437]
 [135.771]
 [146.092]
 [121.949]
 [ 84.769]
 [ 97.515]
 [ 79.454]
 [ 86.961]
 [ 90.007]
 [ 79.983]
 [ 78.412]
 [ 82.149]
 [104.957]
 [ 64.639]
 [116.966]
 [193.437]
 [135.771]
 [146.092]
 [121.949]
 [ 84.769]
 [136.882]
 [ 90.007]
 [ 79.983]
 [ 78.412]
 [ 82.149]
 [104.957]
 [ 64.639]
 [193.437]
 [135.771]
 [146.092]
 [121.949]
 [ 84.769]
 [ 90.007]
 [ 79.983]
 [ 78.412]
 [ 82.149]
 [104.957]
 [ 64.639]
 [117.035]
 [193.437]
 [135.771]
 [146.092]
 [121.949]
 [ 84.769]
 [ 90.007]
 [ 79.983]
 [ 78.412]
 [ 82.149]
 [104.957]
 [ 95.068]
 [109.859]
 [115.036]
 [193.351]
 [135.743]
 [146.1  ]
 [121.985]
 [155.9  ]
 [129.963]
 [ 89.598]
 [ 86.927]
 [125.01 ]
 [ 78.05 ]
 [ 99.289]
 [ 98.84 ]
 [106.376]
 [ 94.828]
 [ 91.973]
 [ 99.993]
 [166.538]
 [ 92.253]
 [152.751]
 [140.767]
 [112.791]
 [ 85.372]
 [ 93.439]
 [ 84.715]
 [ 92.841]
 [166.201]
 [106.114]
 [ 96.462]
 [124.742]
 [127.331]
 [ 86.941]
 [ 91.977]
 [175.868]
 [107.045]
 [157.992]
 [124.821]
 [ 84.547]
 [151.947]
 [ 92.161]
 [ 96.54 ]
 [ 94.922]
 [ 98.96 ]
 [ 93.852]
 [105.253]
 [169.673]
 [197.431]
 [122.394]
 [167.875]
 [122.868]
 [175.861]
 [ 91.962]
 [ 91.977]
 [ 92.015]
 [176.042]
 [ 91.973]
 [175.891]
 [107.051]
 [ 79.006]
 [124.811]
 [169.735]
 [151.922]
 [ 94.867]
 [ 96.543]
 [ 98.949]
 [ 93.785]
 [105.785]
 [169.839]
 [ 97.855]
 [121.689]
 [ 92.   ]
 [166.032]
 [168.072]
 [ 92.015]
 [ 91.973]
 [175.891]
 [107.051]
 [ 79.006]
 [124.811]
 [169.735]
 [151.921]
 [ 96.542]
 [ 98.957]
 [ 93.804]
 [105.775]
 [170.137]
 [195.146]
 [122.956]
 [ 91.625]
 [167.875]
 [122.868]
 [175.861]
 [ 91.962]
 [ 91.977]
 [ 92.015]
 [176.042]
 [ 91.973]
 [175.891]
 [107.051]
 [ 79.006]
 [124.811]
 [169.735]
 [151.922]
 [ 94.867]
 [ 96.543]
 [ 98.949]
 [ 93.785]
 [105.785]
 [169.839]
 [ 97.855]
 [121.689]
 [ 92.   ]
 [166.032]
 [168.072]
 [ 92.013]
 [ 98.992]
 [105.923]
 [ 96.511]
 [ 93.81 ]
 [169.51 ]
 [168.507]
 [157.919]
 [ 98.953]
 [107.034]
 [175.906]
 [102.131]
 [108.161]
 [122.658]
 [ 96.521]
 [105.923]
 [ 96.511]
 [ 93.81 ]
 [169.51 ]
 [168.507]
 [157.919]
 [ 98.953]
 [107.034]
 [175.906]
 [102.131]
 [108.161]
 [122.658]
 [ 96.521]
 [105.923]
 [ 96.511]
 [ 93.81 ]
 [169.51 ]
 [168.507]
 [157.919]
 [ 98.953]
 [107.034]
 [175.906]
 [102.131]
 [166.042]
 [105.906]
 [ 96.512]
 [ 93.848]
 [ 84.931]
 [169.19 ]
 [157.951]
 [ 99.023]
 [107.039]
 [175.855]
 [ 98.074]
 [166.042]
 [104.963]
 [115.81 ]
 [121.929]
 [ 96.559]
 [105.923]
 [ 96.511]
 [ 93.81 ]
 [169.51 ]
 [168.507]
 [157.919]
 [ 98.953]
 [107.034]
 [175.906]
 [102.131]
 [166.042]
 [ 99.165]
 [200.053]
 [ 91.025]
 [166.572]
 [ 74.928]
 [ 83.61 ]
 [112.694]
 [ 76.758]
 [112.708]
 [103.451]
 [116.109]
 [ 99.982]
 [152.48 ]
 [137.24 ]
 [123.46 ]
 [ 99.259]
 [ 99.973]
 [ 91.039]
 [166.602]
 [ 74.934]
 [ 83.615]
 [112.439]
 [ 74.283]
 [112.726]
 [ 84.2  ]
 [116.153]
 [ 99.947]
 [133.173]
 [ 93.615]
 [173.954]
 [ 89.889]
 [ 99.259]
 [ 99.973]
 [ 91.039]
 [166.602]
 [ 74.934]
 [ 83.615]
 [112.439]
 [ 74.283]
 [112.726]
 [ 84.2  ]
 [116.153]
 [ 99.947]
 [133.173]
 [ 93.615]
 [173.954]
 [ 89.861]]

	tempo	danceability	acousticness
count	438.000000	438.000000	438.000000
mean	117.535203	0.579792	0.180185
std	32.090024	0.168656	0.210869
min	63.345000	0.143000	0.000066
25%	93.615000	0.467250	0.025875
50%	107.051000	0.576000	0.094700
75%	137.093500	0.709750	0.247000
max	200.053000	0.925000	0.904000

	Unnamed: 0	album_release_year	danceability	energy	key	loudness	mode	speechiness	acousticness	instrumentalness	liveness	valence	tempo	time_signature	disc_number	duration_ms	is_local	track_number
explicit
False	223.622378	2007.983683	0.581019	0.645393	5.275058	-6.981506	0.731935	0.174589	0.180921	0.013613	0.244634	0.469716	117.232375	3.895105	1.146853	241044.209790	False	7.393939
True	23.000000	2013.444444	0.521333	0.550889	4.777778	-8.107333	0.555556	0.171011	0.145088	0.009180	0.325533	0.353178	131.970000	3.888889	1.222222	327531.555556	False	5.777778

Pandas and Spotify Data¶

Goals this week.¶

Importing Pandas¶

Picking out specific things.¶

Calculating Summary Statistics¶

Testing a hypothesis¶

Results:¶

Exercise¶

	Unnamed: 0	artist_name	artist_id	album_id	album_type	album_images	album_release_date	album_release_year	album_release_date_precision	danceability	energy	key	loudness	mode	speechiness	acousticness	liveness	valence	tempo	track_id	analysis_url	time_signature	artists	available_markets	disc_number	duration_ms	explicit	track_href	is_local	track_name	track_preview_url	track_number	type	track_uri	external_urls.spotify	album_name	key_name	mode_name	key_mode
433	434	Beyoncé	6vWDO969PvNqNYHIOW5v0m	25hVFAxTlDvXbx2X2QkUkE	album	list(height = c(640, 300, 64), url = c("https:...	2003	2003	year	0.619	0.4020	6	-8.054	1	0.0915	0.334	0.177	0.489	99.947	29W4t00Bj5uBwmtuphAhyY	https://api.spotify.com/v1/audio-analysis/29W4...	4	list(href = "https://api.spotify.com/v1/artist...	c("AT", "CH", "DE", "LI")	1	293106	False	https://api.spotify.com/v1/tracks/29W4t00Bj5uB...	False	Dangerously In Love	https://p.scdn.co/mp3-preview/aa580bad422a1e7d...	12	track	spotify:track:29W4t00Bj5uBwmtuphAhyY	https://open.spotify.com/track/29W4t00Bj5uBwmt...	Dangerously In Love (Alben für die Ewigkeit)	F#	major	F# major
434	435	Beyoncé	6vWDO969PvNqNYHIOW5v0m	25hVFAxTlDvXbx2X2QkUkE	album	list(height = c(640, 300, 64), url = c("https:...	2003	2003	year	0.693	0.0457	5	-20.803	0	0.8030	0.423	0.122	0.541	133.173	74LAP3swRIVaUFEcWi90Iv	https://api.spotify.com/v1/audio-analysis/74LA...	3	list(href = "https://api.spotify.com/v1/artist...	c("AT", "CH", "DE", "LI")	1	16360	False	https://api.spotify.com/v1/tracks/74LAP3swRIVa...	False	Beyoncé Interlude	https://p.scdn.co/mp3-preview/f3d6675d78a00d62...	13	track	spotify:track:74LAP3swRIVaUFEcWi90Iv	https://open.spotify.com/track/74LAP3swRIVaUFE...	Dangerously In Love (Alben für die Ewigkeit)	F	minor	F minor
435	436	Beyoncé	6vWDO969PvNqNYHIOW5v0m	25hVFAxTlDvXbx2X2QkUkE	album	list(height = c(640, 300, 64), url = c("https:...	2003	2003	year	0.349	0.2650	11	-12.790	0	0.0542	0.904	0.241	0.311	93.615	1ABGVrP1QEzjMPfzM0KXKt	https://api.spotify.com/v1/audio-analysis/1ABG...	3	list(href = "https://api.spotify.com/v1/artist...	c("AT", "CH", "DE", "LI")	1	163706	False	https://api.spotify.com/v1/tracks/1ABGVrP1QEzj...	False	Gift from Virgo	https://p.scdn.co/mp3-preview/e7e629a306fd3438...	14	track	spotify:track:1ABGVrP1QEzjMPfzM0KXKt	https://open.spotify.com/track/1ABGVrP1QEzjMPf...	Dangerously In Love (Alben für die Ewigkeit)	B	minor	B minor
436	437	Beyoncé	6vWDO969PvNqNYHIOW5v0m	25hVFAxTlDvXbx2X2QkUkE	album	list(height = c(640, 300, 64), url = c("https:...	2003	2003	year	0.683	0.7630	5	-6.588	1	0.1910	0.427	0.184	0.948	173.954	23npFplgR5Uuj5FwyVvy7h	https://api.spotify.com/v1/audio-analysis/23np...	4	list(href = "https://api.spotify.com/v1/artist...	c("AT", "CH", "DE", "LI")	1	246066	False	https://api.spotify.com/v1/tracks/23npFplgR5Uu...	False	Work It Out	https://p.scdn.co/mp3-preview/e9898d1c265ea607...	15	track	spotify:track:23npFplgR5Uuj5FwyVvy7h	https://open.spotify.com/track/23npFplgR5Uuj5F...	Dangerously In Love (Alben für die Ewigkeit)	F	major	F major
437	438	Beyoncé	6vWDO969PvNqNYHIOW5v0m	25hVFAxTlDvXbx2X2QkUkE	album	list(height = c(640, 300, 64), url = c("https:...	2003	2003	year	0.515	0.7050	4	-7.924	1	0.3700	0.240	0.182	0.198	89.861	6IeECiLESgmQDy5IKt5tiA	https://api.spotify.com/v1/audio-analysis/6IeE...	4	list(href = c("https://api.spotify.com/v1/arti...	character(0)	1	517120	False	https://api.spotify.com/v1/tracks/6IeECiLESgmQ...	False	'03 Bonnie & Clyde	NaN	16	track	spotify:track:6IeECiLESgmQDy5IKt5tiA	https://open.spotify.com/track/6IeECiLESgmQDy5...	Dangerously In Love (Alben für die Ewigkeit)	E	major	E major

	Unnamed: 0	artist_name	artist_id	album_id	album_type	album_images	album_release_date	album_release_year	album_release_date_precision	danceability	energy	key	loudness	mode	speechiness	acousticness	instrumentalness	liveness	valence	tempo	track_id	analysis_url	time_signature	artists	available_markets	disc_number	duration_ms	explicit	track_href	is_local	track_name	track_preview_url	track_number	type	track_uri	external_urls.spotify	album_name	key_name	mode_name	key_mode
0	1	Beyoncé	6vWDO969PvNqNYHIOW5v0m	2UJwKSBUz6rtW4QLK74kQu	album	list(height = c(640, 300, 64), url = c("https:...	2014-11-24	2014	day	0.512	0.625	11	-6.764	1	0.0508	0.00308	0.000164	0.346	0.201	129.935	7lUA4P03AhwAw40JHkdyGr	https://api.spotify.com/v1/audio-analysis/7lUA...	4	list(href = "https://api.spotify.com/v1/artist...	c("AD", "AR", "AT", "AU", "BE", "BG", "BO", "B...	1	257653	False	https://api.spotify.com/v1/tracks/7lUA4P03AhwA...	False	Pretty Hurts	https://p.scdn.co/mp3-preview/fd617af2cdf7ea1b...	1	track	spotify:track:7lUA4P03AhwAw40JHkdyGr	https://open.spotify.com/track/7lUA4P03AhwAw40...	BEYONCÉ [Platinum Edition]	B	major	B major
1	2	Beyoncé	6vWDO969PvNqNYHIOW5v0m	2UJwKSBUz6rtW4QLK74kQu	album	list(height = c(640, 300, 64), url = c("https:...	2014-11-24	2014	day	0.436	0.534	1	-9.416	0	0.0773	0.10300	0.006430	0.507	0.325	122.822	7cioKB5CHVzk09SOtTyn0T	https://api.spotify.com/v1/audio-analysis/7cio...	4	list(href = "https://api.spotify.com/v1/artist...	c("AD", "AR", "AT", "AU", "BE", "BG", "BO", "B...	1	369040	True	https://api.spotify.com/v1/tracks/7cioKB5CHVzk...	False	Haunted	https://p.scdn.co/mp3-preview/63fcd43b454b1dc9...	2	track	spotify:track:7cioKB5CHVzk09SOtTyn0T	https://open.spotify.com/track/7cioKB5CHVzk09S...	BEYONCÉ [Platinum Edition]	C#	minor	C# minor
2	3	Beyoncé	6vWDO969PvNqNYHIOW5v0m	2UJwKSBUz6rtW4QLK74kQu	album	list(height = c(640, 300, 64), url = c("https:...	2014-11-24	2014	day	0.589	0.621	5	-6.902	0	0.0468	0.00969	0.001040	0.181	0.401	140.030	6jG2YzhxptolDzLHTGLt7S	https://api.spotify.com/v1/audio-analysis/6jG2...	4	list(href = c("https://api.spotify.com/v1/arti...	c("AD", "AR", "AT", "AU", "BE", "BG", "BO", "B...	1	323480	True	https://api.spotify.com/v1/tracks/6jG2Yzhxptol...	False	Drunk in Love	https://p.scdn.co/mp3-preview/7052c45ae9298c38...	3	track	spotify:track:6jG2YzhxptolDzLHTGLt7S	https://open.spotify.com/track/6jG2YzhxptolDzL...	BEYONCÉ [Platinum Edition]	F	minor	F minor