diff --git a/pull264/_sources/reading.md b/pull264/_sources/reading.md index a8c1f6fa..3106a517 100644 --- a/pull264/_sources/reading.md +++ b/pull264/_sources/reading.md @@ -1469,8 +1469,8 @@ of the nasa_data object. But you can reproduce this using the DEMO_KEY key --> import requests nasa_data_single = requests.get( - "https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&date=2023-07-13" - ).json() + "https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&date=2023-07-13" + ).json() nasa_data_single ``` @@ -1492,8 +1492,8 @@ and there will be 74 items total, one for each day between the start and end dat ```python nasa_data = requests.get( - "https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&start_date=2023-05-01&end_date=2023-07-13" - ).json() + "https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&start_date=2023-05-01&end_date=2023-07-13" + ).json() len(nasa_data) ``` @@ -1513,17 +1513,17 @@ readers who would like to parse JSON data into a `pandas` data frame in their ow ```{code-cell} ipython3 data_dict = { - "date":[], - "title": [], - "copyright" : [], - "url": [] + "date":[], + "title": [], + "copyright" : [], + "url": [] } for item in nasa_data: - if "copyright" not in item: - item["copyright"] = None - for entry in ["url", "title", "date", "copyright"]: - data_dict[entry].append(item[entry]) + if "copyright" not in item: + item["copyright"] = None + for entry in ["url", "title", "date", "copyright"]: + data_dict[entry].append(item[entry]) nasa_df = pd.DataFrame(data_dict) nasa_df diff --git a/pull264/classification1.html b/pull264/classification1.html index d75c578b..401234f8 100644 --- a/pull264/classification1.html +++ b/pull264/classification1.html @@ -871,23 +871,23 @@

5.4.3. Exploring the cancer data
-
+
@@ -981,23 +981,23 @@

5.5. Classification with
-
+

Fig. 5.2 Scatter plot of concavity versus perimeter with new observation represented as a red diamond.#

@@ -1056,23 +1056,23 @@

5.5. Classification with
-
+

Fig. 5.3 Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a malignant @@ -1135,23 +1135,23 @@

5.5. Classification with
-
+

Fig. 5.4 Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a benign @@ -1214,23 +1214,23 @@

5.5. Classification with
-
+

Fig. 5.5 Scatter plot of concavity versus perimeter with three nearest neighbors.#

@@ -1310,23 +1310,23 @@

5.5.1. Distance between points
-
+

Fig. 5.6 Scatter plot of concavity versus perimeter with new observation represented as a red diamond.#

@@ -1505,23 +1505,23 @@

5.5.1. Distance between points
-
+

Fig. 5.7 Scatter plot of concavity versus perimeter with 5 nearest neighbors circled.#

@@ -1715,9 +1715,9 @@

5.5.2. More than two explanatory variabl }); } -

Fig. 5.9 Comparison of K = 3 nearest neighbors with standardized and unstandardized data.#

@@ -2508,23 +2508,23 @@

5.7.1. Centering and scaling
-
+

Fig. 5.10 Close-up of three nearest neighbors for unstandardized data.#

@@ -2618,23 +2618,23 @@

5.7.2. Balancing
-
+

@@ -2715,23 +2715,23 @@

5.7.2. Balancing
-
+

Fig. 5.12 Imbalanced data with 7 nearest neighbors to a new observation highlighted.#

@@ -2789,23 +2789,23 @@

5.7.2. Balancing
-
+

Fig. 5.13 Imbalanced data with background color indicating the decision of the classifier and the points represent the labeled data.#

@@ -2901,23 +2901,23 @@

5.7.2. Balancing
-
+

Fig. 5.14 Upsampled data with background color indicating the decision of the classifier.#

@@ -3440,23 +3440,23 @@

5.7.3. Missing data
-
+
diff --git a/pull264/classification2.html b/pull264/classification2.html index a54d79a4..7052d0e9 100644 --- a/pull264/classification2.html +++ b/pull264/classification2.html @@ -803,23 +803,23 @@

6.5. Evaluating performance with
-
+
@@ -1525,32 +1525,32 @@

6.6.1. Cross-validation6.6.1. Cross-validation6.6.1. Cross-validation6.6.1. Cross-validation6.6.2. Parameter value selection
-
+

Fig. 6.5 Plot of estimated accuracy versus the number of neighbors.#

@@ -2252,23 +2252,23 @@

6.6.3. Under/Overfitting
-
+

Fig. 6.6 Plot of accuracy estimate versus number of neighbors for many K values.#

@@ -2343,23 +2343,23 @@

6.6.3. Under/Overfitting
-
+

Fig. 6.7 Effect of K in overfitting and underfitting.#

@@ -2644,23 +2644,23 @@

6.8.1. The effect of irrelevant predicto
-
+

Fig. 6.9 Effect of inclusion of irrelevant predictors.#

@@ -2723,23 +2723,23 @@

6.8.1. The effect of irrelevant predicto
-
+

Fig. 6.10 Tuned number of neighbors for varying number of irrelevant predictors.#

@@ -2793,23 +2793,23 @@

6.8.1. The effect of irrelevant predicto
-
+

Fig. 6.11 Accuracy versus number of irrelevant predictors for tuned and untuned number of neighbors.#

@@ -3273,23 +3273,23 @@

6.8.3. Forward selection in
-
+

Fig. 6.12 Estimated accuracy versus the number of predictors for the sequence of models built using forward selection.#

diff --git a/pull264/clustering.html b/pull264/clustering.html index b902a4e5..0ed7bbe1 100644 --- a/pull264/clustering.html +++ b/pull264/clustering.html @@ -630,23 +630,23 @@

9.3. Clustering
-
+

Fig. 9.2 Scatter plot of standardized bill length versus standardized flipper length.#

@@ -725,23 +725,23 @@

9.3. Clustering
-
+

Fig. 9.3 Scatter plot of standardized bill length versus standardized flipper length with colored groups.#

@@ -1362,23 +1362,23 @@

9.5. K-means in Python
-
+

Fig. 9.13 The data colored by the cluster assignments returned by K-means.#

@@ -1528,15 +1528,15 @@

9.5. K-means in Python
/opt/conda/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
   super()._check_params_vs_input(X, default_n_init=10)
 /opt/conda/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
   super()._check_params_vs_input(X, default_n_init=10)
 /opt/conda/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
   super()._check_params_vs_input(X, default_n_init=10)
-
- -
/opt/conda/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
+/opt/conda/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
   super()._check_params_vs_input(X, default_n_init=10)
 /opt/conda/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
   super()._check_params_vs_input(X, default_n_init=10)
@@ -1629,23 +1629,23 @@ 
9.5. K-means in Python
 

 
-

+

 

 
Fig. 9.14 A plot showing the total WSSD versus the number of clusters.#

 

diff --git a/pull264/inference.html b/pull264/inference.html
index 80ff5b40..0c493066 100644
--- a/pull264/inference.html
+++ b/pull264/inference.html
@@ -1224,23 +1224,23 @@ 
10.4.1. Sampling distributions for propo
 

 

 
-

+

 

 
Fig. 10.2 Sampling distribution of the sample proportion for sample size 40.#

 

@@ -1348,23 +1348,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 
Fig. 10.3 Population distribution of price per night (dollars) for all Airbnb listings in Vancouver, Canada.#

 

@@ -1470,23 +1470,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 
Fig. 10.4 Distribution of price per night (dollars) for sample of 40 Airbnb listings.#

 

@@ -1685,23 +1685,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 
Fig. 10.5 Sampling distribution of the sample means for sample size of 40.#

 

@@ -1781,23 +1781,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+
@@ -1863,23 +1863,23 @@

10.4.2. Sampling distributions for means
-
+

@@ -2016,23 +2016,23 @@

10.5.1. Overview
-
+

Fig. 10.8 Comparison of samples of different sizes from the population.#

@@ -2621,23 +2621,23 @@

10.5.2. Bootstrapping in Python
-
+
@@ -2721,23 +2721,23 @@

10.5.2. Bootstrapping in Python
-
+
@@ -3017,23 +3017,23 @@

10.5.2. Bootstrapping in Python
-
+
@@ -3290,23 +3290,23 @@

10.5.2. Bootstrapping in Python
-
+
@@ -3367,23 +3367,23 @@

10.5.2. Bootstrapping in Python
-
+
@@ -3543,23 +3543,23 @@

10.5.3. Using the bootstrap to calculate
-
+
diff --git a/pull264/intro.html b/pull264/intro.html index c08b0806..1c3f2954 100644 --- a/pull264/intro.html +++ b/pull264/intro.html @@ -2036,23 +2036,23 @@

1.11.1. Using
-
+

Fig. 1.8 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue#

@@ -2140,23 +2140,23 @@

1.11.1. Using
-
+

Fig. 1.9 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with x and y labels. Note that this visualization is not done yet; there are still improvements to be made.#

@@ -2226,23 +2226,23 @@

1.11.1. Using
-
+

Fig. 1.10 Horizontal bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue. There are no more serious issues with this visualization, but it could be refined further.#

@@ -2314,23 +2314,23 @@

1.11.1. Using
-
+

Fig. 1.11 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with bars reordered.#

@@ -2440,23 +2440,23 @@

1.11.3. Putting it all together
-
+

Fig. 1.12 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue#

diff --git a/pull264/reading.html b/pull264/reading.html index 465e000a..6003d6fa 100644 --- a/pull264/reading.html +++ b/pull264/reading.html @@ -3228,8 +3228,8 @@

Accessing the NASA API
import requests
 
 nasa_data_single = requests.get(
-	"https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&date=2023-07-13"
-	).json()
+    "https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&date=2023-07-13"
+    ).json()
 nasa_data_single
@@ -3253,8 +3253,8 @@

Accessing the NASA APInasa_data_single object), and there will be 74 items total, one for each day between the start and end dates:

nasa_data = requests.get(
-	"https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&start_date=2023-05-01&end_date=2023-07-13"
-	).json()
+    "https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&start_date=2023-05-01&end_date=2023-07-13"
+    ).json()
 len(nasa_data)
@@ -3276,17 +3276,17 @@

Accessing the NASA API 
data_dict = {
-	"date":[],
-	"title": [],
-	"copyright" : [],
-	"url": []
+    "date":[],
+    "title": [],
+    "copyright" : [],
+    "url": []
 }
 
 for item in nasa_data:
-	if "copyright" not in item:
-		item["copyright"] = None
-	for entry in ["url", "title", "date", "copyright"]:
-		data_dict[entry].append(item[entry])
+    if "copyright" not in item:
+        item["copyright"] = None
+    for entry in ["url", "title", "date", "copyright"]:
+        data_dict[entry].append(item[entry])
 
 nasa_df = pd.DataFrame(data_dict)
 nasa_df
diff --git a/pull264/regression1.html b/pull264/regression1.html
index 0d4ac7bd..98941283 100644
--- a/pull264/regression1.html
+++ b/pull264/regression1.html
@@ -677,23 +677,23 @@ 
7.4. Exploring a data set
 

 
-

+

 

 
Fig. 7.1 Scatter plot of price (USD) versus house size (square feet).#

 

@@ -806,23 +806,23 @@ 
7.5. K-nearest neighbors regression
 

 
-

+

 

 
Fig. 7.2 Scatter plot of price (USD) versus house size (square feet) with vertical line indicating 2,000 square feet on x-axis.#

 

@@ -994,23 +994,23 @@ 
7.5. K-nearest neighbors regression
 

 
-

+

 

 
Fig. 7.3 Scatter plot of price (USD) versus house size (square feet) with lines to 5 nearest neighbors.#

 

@@ -1084,23 +1084,23 @@ 
7.5. K-nearest neighbors regression
 

 
-

+

 

 
Fig. 7.4 Scatter plot of price (USD) versus house size (square feet) with predicted price for a 2,000 square-foot house based on 5 nearest neighbors represented as a red dot.#

 

@@ -1223,23 +1223,23 @@ 
7.6. Training, evaluating, and tuning th
 

 

 
-

+

 

 
Fig. 7.5 Scatter plot of price (USD) versus house size (square feet) with example predictions (blue line) and the error in those predictions compared with true response values for three selected observations (vertical red lines).#

 

@@ -1615,23 +1615,23 @@ 
7.6. Training, evaluating, and tuning th
 

 

 
-

+

 

 
Fig. 7.6 Effect of the number of neighbors on the RMSPE.#

 

@@ -1713,23 +1713,23 @@ 
7.7. Underfitting and overfitting
 

 
-

+

 

 
Fig. 7.7 Predicted values for house price (represented as a orange line) from KNN regression models for six different values for \(K\).#

 

@@ -1924,23 +1924,23 @@ 
7.8. Evaluating on the test set
 

 
-

+

 

 
Fig. 7.8 Predicted values of house price (orange line) for the final KNN regression model.#

 

@@ -2031,23 +2031,23 @@ 
7.9. Multivariable KNN regression
 

 
-

+

 

 
Fig. 7.9 Scatter plot of the sale price of houses versus the number of bedrooms.#

 

@@ -2257,9 +2257,9 @@ 
7.9. Multivariable KNN regression
-

Fig. 8.1 Scatter plot of sale price versus size with line of best fit for subset of the Sacramento housing data.#

@@ -528,23 +528,23 @@

8.3. Simple linear regression
-
+

Fig. 8.2 Scatter plot of sale price versus size with line of best fit and a red dot at the predicted sale price for a 2,000 square-foot home.#

@@ -604,23 +604,23 @@

8.3. Simple linear regression
-
+

Fig. 8.3 Scatter plot of sale price versus size with many possible lines that could be drawn through the data points.#

@@ -680,23 +680,23 @@

8.3. Simple linear regression
-
+

Fig. 8.4 Scatter plot of sale price versus size with red lines denoting the vertical distances between the predicted values and the observed data points.#

@@ -930,23 +930,23 @@

8.4. Linear regression in Python
-
+

Fig. 8.5 Scatter plot of sale price versus size with line of best fit for the full Sacramento housing data.#

@@ -1009,23 +1009,23 @@

8.5. Comparing simple linear and KNN reg
-
+

Fig. 8.6 Comparison of simple linear regression and KNN regression.#

@@ -1195,9 +1195,9 @@

8.6. Multivariable linear regression -

Fig. 8.8 Scatter plot of a subset of the data, with outlier highlighted in red.#

@@ -1422,23 +1422,23 @@

8.7.1. Outliers
-
+

Fig. 8.9 Scatter plot of the full data, with outlier highlighted in red.#

@@ -1504,23 +1504,23 @@

8.7.2. Multicollinearity
-
+

Fig. 8.10 Scatter plot of house size (in square feet) measured by person 1 versus house size (in square feet) measured by person 2.#

@@ -1693,23 +1693,23 @@

8.8. Designing new predictors
-
+

Fig. 8.11 Example of a data set with a nonlinear relationship between the predictor and the response.#

@@ -1781,23 +1781,23 @@

8.8. Designing new predictors
-
+

Fig. 8.12 Relationship between the transformed predictor and the response.#

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4.5.1. Scatter plots and line plots: the
-
+

Fig. 4.2 Scatter plot of atmospheric concentration of CO\(_{2}\) over time.#

@@ -838,23 +838,23 @@

4.5.1. Scatter plots and line plots: the
-
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Fig. 4.3 Line plot of atmospheric concentration of CO\(_{2}\) over time.#

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4.5.1. Scatter plots and line plots: the
-
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Fig. 4.4 Line plot of atmospheric concentration of CO\(_{2}\) over time with clearer axes and labels.#

@@ -1040,23 +1040,23 @@

4.5.1. Scatter plots and line plots: the
-
+

Fig. 4.5 Line plot of atmospheric concentration of CO\(_{2}\) from 1990 to 1995.#

@@ -1252,23 +1252,23 @@

4.5.2. Scatter plots: the Old Faithful e
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+

Fig. 4.6 Scatter plot of waiting time and eruption time.#

@@ -1339,23 +1339,23 @@

4.5.2. Scatter plots: the Old Faithful e
-
+

Fig. 4.7 Scatter plot of waiting time and eruption time with clearer axes and labels.#

@@ -1420,23 +1420,23 @@

4.5.2. Scatter plots: the Old Faithful e
-
+

Fig. 4.8 Scatter plot of waiting time and eruption time with black points.#

@@ -1662,23 +1662,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.9 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home#

@@ -1756,23 +1756,23 @@

4.5.3. Axis transformation and colored s
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Fig. 4.10 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home with x and y labels.#

@@ -1930,23 +1930,23 @@

4.5.3. Axis transformation and colored s
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Fig. 4.11 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home with log-adjusted x and y axes.#

@@ -2026,23 +2026,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.12 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home with log-adjusted x and y axes. Only the major gridlines are shown. The suffix “k” indicates 1,000 (“kilo”), while the suffix “M” indicates 1,000,000 (“million”).#

@@ -2235,23 +2235,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.13 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home.#

@@ -2372,23 +2372,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.14 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category.#

@@ -2467,23 +2467,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.15 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category with the legend edited.#

@@ -2578,23 +2578,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.16 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category with custom colors.#

@@ -2680,23 +2680,23 @@

4.5.3. Axis transformation and colored s
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+

Fig. 4.17 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category with custom colors and mouse hover tooltip.#

@@ -3123,23 +3123,23 @@

4.5.4. Bar plots: the island landmass da
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+

Fig. 4.18 Bar plot of all Earth’s landmasses’ size with squished labels.#

@@ -3224,23 +3224,23 @@

4.5.4. Bar plots: the island landmass da
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Fig. 4.19 Bar plot of size for Earth’s largest 12 landmasses.#

@@ -3327,23 +3327,23 @@

4.5.4. Bar plots: the island landmass da
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Fig. 4.20 Bar plot of size for Earth’s largest 12 landmasses colored by whether its a continent with clearer axes and labels.#

@@ -3555,23 +3555,23 @@

4.5.5. Histograms: the Michelson speed o
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Fig. 4.21 A bar chart of Michelson’s speed of light data.#

@@ -3646,23 +3646,23 @@

4.5.5. Histograms: the Michelson speed o
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Fig. 4.22 Histogram of Michelson’s speed of light data.#

@@ -3768,23 +3768,23 @@

Adding layers to an
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Fig. 4.23 Histogram of Michelson’s speed of light data with vertical line indicating the true speed of light.#

@@ -3862,23 +3862,23 @@

Adding layers to an
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Fig. 4.24 Histogram of Michelson’s speed of light data colored by experiment.#

@@ -3986,23 +3986,23 @@

Adding layers to an
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Fig. 4.25 Histogram of Michelson’s speed of light data colored by experiment as a categorical variable.#

@@ -4089,23 +4089,23 @@

Adding layers to an
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Fig. 4.26 Histogram of Michelson’s speed of light data split vertically by experiment.#

@@ -4322,23 +4322,23 @@

Adding layers to an
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Fig. 4.27 Histogram of relative error split vertically by experiment with clearer axes and labels#

@@ -4412,23 +4412,23 @@

Choosing a binwidth for histograms
-
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Fig. 4.28 Histogram of Michelson’s speed of light data.#

@@ -4500,23 +4500,23 @@

Choosing a binwidth for histograms
-
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Fig. 4.29 Effect of varying number of max bins on histograms.#

diff --git a/pull264/wrangling.html b/pull264/wrangling.html index 2badfcda..fec44d3c 100644 --- a/pull264/wrangling.html +++ b/pull264/wrangling.html @@ -5055,7 +5055,7 @@

3.9. Performing operations on groups of

-
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f9599f445d0>
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<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f69a4c08650>