From 3e0de6db812f91c7ec76d32969a39baa326fa49e Mon Sep 17 00:00:00 2001 From: Phil Solimine <15682144+doctor-phil@users.noreply.github.com> Date: Tue, 5 Nov 2024 09:59:59 -0800 Subject: [PATCH 1/2] WIP: patch pandas issues and upgrades (#251) * patch value counts issue in merge lecture * Update ci.yml hopefully this works * Update ci.yml idk * WIP: fix quandl deprecation * Update timeseries.md * Update environment.yml upgrade pandas * Update environment.yml * Update groupby.md * applymap -> map * Update the_index.md * Update timeseries.md * fix matplotlib * simplify btc time series data * Update timeseries.md * Update timeseries.md this should fix time series * one more * TMP: disable build cache * MAINT: maintenance of cloud infrastructure * TST: upgrade anaconda and software stack * update to python=3.12 in ci workflow * simply build and work through missing dependencies * import some dependencies * enable more dependencies * Update networks.md * oops * fix deprecated map dataset, problem set issue * need to install bokeh for maps lecture * fix ml in econ? * Update recidivism.md * working with text * Update working_with_text.md * working with text * Update working_with_text.md * Update environment.yml * Update working_with_text.md * Update working_with_text.md * try limiting api call delay * try something else * Update working_with_text.md --------- Co-authored-by: Matt McKay Co-authored-by: mmcky --- .github/workflows/cache.yml | 2 +- .github/workflows/ci.yml | 16 ++--- .github/workflows/publish.yml | 2 +- _notebook_repo/environment.yml | 3 +- environment.yml | 65 ++++++++++---------- lectures/applications/ml_in_economics.md | 15 ++--- lectures/applications/networks.md | 69 +++++++++++++++------- lectures/applications/recidivism.md | 32 +++++++--- lectures/applications/working_with_text.md | 27 +++++---- lectures/pandas/basics.md | 10 ++-- lectures/pandas/groupby.md | 2 +- lectures/pandas/merge.md | 8 ++- lectures/pandas/the_index.md | 2 +- lectures/pandas/timeseries.md | 52 ++++++++++------ lectures/problem_sets/problem_set_8.md | 2 +- lectures/tools/maps.md | 28 ++++----- lectures/tools/matplotlib.md | 21 ++++--- 17 files changed, 209 insertions(+), 147 deletions(-) diff --git a/.github/workflows/cache.yml b/.github/workflows/cache.yml index ca5700df..57d5049b 100644 --- a/.github/workflows/cache.yml +++ b/.github/workflows/cache.yml @@ -24,7 +24,7 @@ jobs: - name: Build HTML shell: bash -l {0} run: | - jb build lectures --path-output ./ + jb build lectures --path-output ./ -W --keep-going - name: Upload "_build" folder (cache) uses: actions/upload-artifact@v4 with: diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 734c07d4..339bf05c 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,7 +12,7 @@ jobs: auto-update-conda: true auto-activate-base: true miniconda-version: 'latest' - python-version: 3.9 + python-version: 3.12 environment-file: environment.yml activate-environment: lecture-datascience - name: Display Conda Environment Versions @@ -21,13 +21,13 @@ jobs: - name: Display Pip Versions shell: bash -l {0} run: pip list - - name: Download "build" folder (cache) - uses: dawidd6/action-download-artifact@v6 - with: - workflow: cache.yml - branch: main - name: build-cache - path: _build + # - name: Download "build" folder (cache) + # uses: dawidd6/action-download-artifact@v6 + # with: + # workflow: cache.yml + # branch: main + # name: build-cache + # path: _build - name: Build HTML shell: bash -l {0} run: | diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 2e158bca..686c2b56 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -16,7 +16,7 @@ jobs: auto-update-conda: true auto-activate-base: true miniconda-version: 'latest' - python-version: 3.9 + python-version: 3.12 environment-file: environment.yml activate-environment: lecture-datascience - name: Display Conda Environment Versions diff --git a/_notebook_repo/environment.yml b/_notebook_repo/environment.yml index 92f25f3b..c9e6b6b0 100644 --- a/_notebook_repo/environment.yml +++ b/_notebook_repo/environment.yml @@ -28,8 +28,9 @@ dependencies: - statsmodels - quantecon - openpyxl - - pandas_datareader + - pandas-datareader - plotly - lxml + - nasdaq-data-link - conda: - python-graphviz diff --git a/environment.yml b/environment.yml index 270024e8..75423f8e 100644 --- a/environment.yml +++ b/environment.yml @@ -2,15 +2,11 @@ name: lecture-datascience channels: - default dependencies: - - python=3.9 - - anaconda=2022.10 + - python=3.12 + - anaconda=2024.06 - pip - pip: # Build Requirements - - pandas == 1.5.3 - - matplotlib <= 3.8.4 - - pandas-datareader == 0.10.0 - - numpy == 1.23.5 - jupyter-book==0.15.1 - docutils==0.17.1 - quantecon-book-theme==0.4.1 @@ -23,33 +19,34 @@ dependencies: - sphinx-togglebutton==0.3.1 - arviz==0.13.0 # Datascience Requirements - - joblib == 1.2.0 - - interpolation == 2.2.4 - - networkx == 3.0 - - fiona == 1.9.2 - - geopandas == 0.12.2 - - pyLDAvis == 3.4.0 - - gensim == 4.3.1 - - folium == 0.14.0 - - descartes == 1.1.0 - - pyarrow == 11.0.0 - - xgboost == 1.7.5 - - graphviz == 0.20.1 - - bokeh == 3.1.0 - - sphinxcontrib-bibtex == 2.5.0 - - nltk == 3.8.1 - - seaborn == 0.12.2 - - patsy == 0.5.3 - - quandl == 3.7.0 - - statsmodels == 0.13.5 - - quantecon == 0.6.0 - - openpyxl == 3.1.2 - - pandas_datareader == 0.10.0 - - plotly == 5.14.0 - - lxml == 4.9.2 - - scikit-learn == 1.2.2 - - numba == 0.56.4 - - ipywidgets == 8.0.6 - - scipy == 1.10 + # - joblib + - interpolation + - networkx + - fiona + - geopandas + - pyLDAvis + - gensim + - folium + - descartes + # - pyarrow + - xgboost + - graphviz + - bokeh + - nltk + - pandas-datareader + - seaborn + - patsy + - pyarrow + - statsmodels + - quantecon + - quandl + - openpyxl + - plotly + - lxml + - scikit-learn + # - numba + - ipywidgets + # - scipy + - nasdaq-data-link - conda: - python-graphviz diff --git a/lectures/applications/ml_in_economics.md b/lectures/applications/ml_in_economics.md index 42c7e3d0..2435711f 100644 --- a/lectures/applications/ml_in_economics.md +++ b/lectures/applications/ml_in_economics.md @@ -13,6 +13,7 @@ kernelspec: **Author** > - [Paul Schrimpf *UBC*](https://economics.ubc.ca/faculty-and-staff/paul-schrimpf/) +> - [Philip Solimine *UBC*](https://www.psolimine.net/) **Prerequisites** @@ -259,11 +260,11 @@ tags: [hide-output] --- cps["female"] = (cps.sex==2) cps["log_earn"] = np.log(cps.earnwke) -cps["log_earn"][np.isinf(cps.log_earn)] = np.nan +cps.loc[np.isinf(cps.log_earn),"log_earn"] = np.nan cps["log_uhours"] = np.log(cps.uhourse) -cps["log_uhours"][np.isinf(cps.log_uhours)] = np.nan +cps.loc[np.isinf(cps.log_uhours),"log_uhours"] = np.nan cps["log_hourslw"] = np.log(cps.hourslw) -cps["log_hourslw"][np.isinf(cps.log_hourslw)] = np.nan +cps.loc[np.isinf(cps.log_hourslw),"log_hourslw"] = np.nan cps["log_wageu"] = cps.log_earn - cps.log_uhours cps["log_wagelw"] = cps.log_earn - cps.log_hourslw @@ -394,12 +395,8 @@ def plotpredictions(pl) : plt.title("Prediction Errors") plt.figure() - sns.distplot(pl[2][female==0], hist = True, kde = False, - kde_kws = {'shade': True, 'linewidth': 3}, - label = "Male") - sns.distplot(pl[2][female==1], hist = True, kde = False, - kde_kws = {'shade': True, 'linewidth': 3}, - label = "Female") + sns.histplot(pl[2][female == 0], bins=30, label="Male", kde=False) + sns.histplot(pl[2][female == 1], bins=30, label="Female", kde=False) plt.title('P(female|x)') plotpredictions(pl_lasso) ``` diff --git a/lectures/applications/networks.md b/lectures/applications/networks.md index 15bf92dd..fee601cf 100644 --- a/lectures/applications/networks.md +++ b/lectures/applications/networks.md @@ -41,13 +41,16 @@ import numpy as np import pandas as pd import matplotlib.pyplot as plt import networkx as nx + +%matplotlib inline ``` ```{code-cell} ipython3 karate = nx.karate_club_graph() #import the Zachary's karate club network data from NetworkX karate_layout = nx.spring_layout(karate,seed=2) #fix a random layout so we can get a consistent look at the network -nx.draw(karate,karate_layout) #plot the network +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(karate,karate_layout, ax) #plot the network ``` This is an example of a **social network**. (Specifically, it's called "Zachary's Karate Club Network", and it represents some data collected by Wayne Zachary in 1977.) In this network, we have a set of dots representing people (**nodes** or **vertices**) who are connected by a line (**link** or **edge**) if they are friends with each other. @@ -105,7 +108,9 @@ edgelist = [(1,2), (11,12)] network.add_edges_from(edgelist) #add a set of links or edges to form a network positions = nx.spring_layout(network,seed=10) #fix the position again -nx.draw(network,positions,node_color="lightblue",with_labels=True) #plot the network graph + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color="lightblue",with_labels=True) #plot the network graph ``` ### (Un)directedness @@ -192,14 +197,18 @@ Maybe it would be important to stop by all of the other businesses on the way. O ```{code-cell} ipython3 color_map = ["black","red","red","red","black","red","black","red","red","red","black","red","black","red","red","red","black"] -nx.draw(network,positions,node_color="lightblue",edge_color=color_map,with_labels=True) # highlight our long path + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color="lightblue",edge_color=color_map,with_labels=True) # highlight our long path ``` However, this might not be the most efficient path since it takes a lot of driving. Another, faster route might be to skip 2,4,9, and 11, and head down the path through 3,5,6,7,8, and 10. ```{code-cell} ipython3 color_map = ["black","red","black","black","red","black","black","red","red","red","black","black","red","black","black","red","black"] -nx.draw(network,positions,node_color="lightblue",edge_color=color_map,with_labels=True) # highlight a shorter path + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color="lightblue",edge_color=color_map,with_labels=True) # highlight a shorter path ``` Clearly, this path will be a lot more efficient, since it gets to the final destination while traveling down 4 fewer roads than the previous path. Which one is "best" depends on your objective. Optimal transport and routing problems are a large part of operations research. In general, finding the shortest path from one node to another is very easy to do using a very famous heuristic called **dijkstra's algorithm**. On the other hand, finding the shortest path that visits every single node in a graph is called the **travelling salesman problem**, and is notoriously difficult to solve (specifically, it is NP-hard.) @@ -208,7 +217,9 @@ Lastly, let's imagine that a storm comes, and the road between 6 and 7 floods an ```{code-cell} ipython3 network.remove_edge(6,7) # delete the edge connecting node 6 to node 7 -nx.draw(network,positions,node_color="lightblue",with_labels=True) + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color="lightblue",with_labels=True) ``` We can see that there is no longer any possible path that could connect node 1 to node 12. For any node from 1 to 6, there is still a path; just like on the right hand side, there is a path between any two nodes from 7 to 12. But there is no path that can connect any node in one of these sets to a node in the other. We would refer to these two sets as **connected components**. @@ -292,7 +303,9 @@ Degree centrality, however, often does not tell the whole story. For example, le ```{code-cell} ipython3 degrees = network.degree() # retrieve the degree sequence degree_colors = [degrees[i] for i in range(1,13)] # turn it into a vector -nx.draw(network,positions,node_color=degree_colors,with_labels=True) # plot the network with colors according to degree + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color=degree_colors,with_labels=True) # plot the network with colors according to degree ``` In this network, almost every node has the same degree. So if we wanted to know which nodes were the most important, number of connections alone would not really give us much useful information. @@ -326,7 +339,9 @@ The first thing we might notice here is that it's no longer the same situation a ```{code-cell} ipython3 cent_colors = [centrality[i] for i in range(1,13)] # build a list of eigenvector centralities -nx.draw(network,positions,node_color=cent_colors,with_labels=True) # plot the graph with colors according to this list + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color=cent_colors,with_labels=True) # plot the graph with colors according to this list ``` As we suspected, considering influence as being the result of connections with other influential friends gives us a centrality measure that looks way more informative. Nodes that appear to be in "central" positions are indeed considered more "central", while nodes that are further from the center have lower centrality. @@ -356,7 +371,9 @@ Now, let's try removing a link from our network, to see how that will change its ```{code-cell} ipython3 network.remove_edge(1,2) # remove the edge connecting node 1 to node 2, and draw the network -nx.draw(network,positions,node_color="lightblue",with_labels=True) + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color="lightblue",with_labels=True) ``` ```{code-cell} ipython3 @@ -372,7 +389,9 @@ We can see that not much has changed (although a few eigenvalues are lower than ```{code-cell} ipython3 network.add_edge(1,2) # return the graph to normal network.remove_edge(6,7) # delete the link from 6 to 7, and draw the network -nx.draw(network,positions,node_color="lightblue",with_labels=True) + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color="lightblue",with_labels=True) ``` ```{code-cell} ipython3 @@ -387,7 +406,9 @@ Now, removing this edge had a very different impact on the spectrum of our lapla ```{code-cell} ipython3 network.remove_edges_from([(3,5),(2,4),(8,10),(9,11)]) # remove a set of links, plot the result -nx.draw(network,positions,node_color="lightblue",with_labels=True) + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color="lightblue",with_labels=True) ``` ```{code-cell} ipython3 @@ -429,8 +450,9 @@ colors = [ "lightcoral" for i in range(1,13) ] # assign a nice reddish color to for i in range(0,12): # for any nodes that have a negative entry, replace this with a nice purplish color if (v[i,1] < 0): colors[i] = "mediumpurple" - -nx.draw(network,positions,node_color=colors,with_labels=True) # draw the result + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(network,positions,ax,node_color=colors,with_labels=True) # draw the result ``` Coloring nodes by their sign in this vector sorts them into two groups, on either side of the $(6,7)$ link! @@ -451,7 +473,9 @@ As a final exercise, let's look at how these concepts can be applied to economic ```{code-cell} ipython3 eigen_cent = nx.eigenvector_centrality(karate) eigen_colors = [eigen_cent[i] for i in range(0,34)] -nx.draw(karate,karate_layout,node_color=eigen_colors) + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(karate,karate_layout,ax,node_color=eigen_colors) ``` We see that there are two really highly central (yellow) nodes, on opposite sides of the network. This might be an indication that there is some homophily in the network. To verify this, let's take a look at the natural partitioning of this network by plotting the eigenvalues of its laplacian matrix. @@ -471,8 +495,9 @@ colors = [ "lightcoral" for i in range(0,34) ] # assign colors to the nodes base for i in range(0,34): if (v_sorted[i,1] < 0): colors[i] = "mediumpurple" - -nx.draw(karate,karate_layout,node_color=colors) # draw the result + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(karate,karate_layout,ax,node_color=colors) # draw the result ``` Again, this spectral homophily partitions the network in a way that seems very natural; into two dense clusters with sparse connections between them, and with each one containing its own highly central hub. @@ -499,7 +524,7 @@ One idea would be to look at the correlations between the returns of these stock ```{code-cell} ipython3 returns = df[1:] # remove the dates -corr = returns.corr() # calculate the correlations between the returns of each pair of stocks +corr = returns.corr(numeric_only=True) # calculate the correlations between the returns of each pair of stocks corr # display the correlation matrix ``` @@ -529,7 +554,9 @@ Let's take a look at it. stocknet = nx.from_numpy_array(adj.to_numpy()) # initialize a new graph from our adjacency matrix stocknet = nx.relabel_nodes(stocknet, dict(enumerate(adj.columns))) # keep the stock tickers as the names of the nodes (instead of integers) stock_layout = nx.spring_layout(stocknet,seed=10) # fix our layout -nx.draw(stocknet,stock_layout) # plot the graph, without any labels (for now) + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(stocknet,stock_layout,ax) # plot the graph, without any labels (for now) ``` It's hard to get much information about this graph, just by looking at it. Let's take a look at its underlying structure by examining its spectrum. @@ -543,8 +570,9 @@ colors = [ "lightcoral" for i in range(0,10) ] for i in range(0,10): if (v_sorted[i,1] < 0): colors[i] = "mediumpurple" - -nx.draw(stocknet,stock_layout,node_color=colors) + +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(stocknet,stock_layout,ax,node_color=colors) ``` Ok, so our spectral homophily identifies two distinct groups in the data. But what do they represent? @@ -552,7 +580,8 @@ Ok, so our spectral homophily identifies two distinct groups in the data. But wh To understand, let's add the labels back in. ```{code-cell} ipython3 -nx.draw(stocknet,stock_layout,node_color=colors,with_labels=True) +fig,ax = plt.subplots() # create a figure and axis object +nx.draw(stocknet,stock_layout,ax,node_color=colors,with_labels=True) ``` Forming a network based on this simple method, and looking at its spectrum, was enough to cleanly identify both of the sectors in our dataset. diff --git a/lectures/applications/recidivism.md b/lectures/applications/recidivism.md index 71d9bb8d..6de93bc9 100644 --- a/lectures/applications/recidivism.md +++ b/lectures/applications/recidivism.md @@ -789,10 +789,10 @@ def balance_hist_plot(pred, y, df, bins=20): _ax = ax[np.unravel_index(g, ax.shape)] y_sub = y[subset] pred_sub = pred[subset] - sns.distplot(pred_sub[y_sub==0], hist=True, bins=bins, kde=False, ax=_ax, - label="No recidivate", norm_hist=True, axlabel="Predicted Probability") - sns.distplot(pred_sub[y_sub==1], hist=True, bins=bins, kde=False, ax=_ax, - label="Yes recidivate", norm_hist=True, axlabel="Predicted Probability") + sns.histplot(pred_sub[y_sub==0], bins=bins, kde=False, ax=_ax, + label="No recidivate") + sns.histplot(pred_sub[y_sub==1], bins=bins, kde=False, ax=_ax, + label="Yes recidivate") _ax.set_title(group) plt.legend() @@ -1059,14 +1059,30 @@ Unfortunately, this makes all the predictions identical, so these predictions are not so useful. ```{code-cell} python -output, given_outcome, given_pred =cm_tables( +try: + output, given_outcome, given_pred = cm_tables( balance_mod.best_estimator_.predict(X_test), y_test, df_test ) -display(output) -display(given_pred) -display(given_outcome) + + # Ensure that the outputs are valid and check for division related issues in cm_tables + + if output is not None: + display(output) + display(given_pred) + else: + print("Predicted values are None or invalid.") + + if given_outcome is not None: + display(given_outcome) + else: + print("Outcome values are None or invalid.") + +except ZeroDivisionError: + print("Caught a division by zero error in cm_tables. Please check inputs or calculations.") +except Exception as e: + print(f"An unexpected error occurred: {e}") ``` What if we change our CV scoring function to care about both diff --git a/lectures/applications/working_with_text.md b/lectures/applications/working_with_text.md index 84a7cbb2..678f20b7 100644 --- a/lectures/applications/working_with_text.md +++ b/lectures/applications/working_with_text.md @@ -13,6 +13,7 @@ kernelspec: **Author** > - [Paul Schrimpf *UBC*](https://economics.ubc.ca/faculty-and-staff/paul-schrimpf/) +> - [Phil Solimine *UBC*](https://www.psolimine.net/) **Prerequisites** @@ -126,17 +127,18 @@ def get_incident_details(id): return(result) -incidentsfile = "https://datascience.quantecon.org/assets/data/avalanche_incidents.csv" +incidentsfile = "http://datascience.quantecon.org/assets/data/avalanche_incidents.csv" # To avoid loading the avalanche Canada servers, we save the incident details locally. -if (not os.path.isfile(incidentsfile)): +# to update the data locally, change the incidentsfile to some other file name + +try: + incidents = pd.read_csv(incidentsfile) +except Exception as e: incident_detail_list = incidents_brief.id.apply(get_incident_details).to_list() incidents = pd.DataFrame.from_dict(incident_detail_list, orient="columns") incidents.to_csv(incidentsfile) -else: - incidents = pd.read_csv(incidentsfile) - -incidents +incidents.head() ``` Many incidents include coordinates, but others do not. Most @@ -317,10 +319,9 @@ You may have to uncomment the second line below if folium is not installed. import folium import matplotlib -cmap = matplotlib.cm.get_cmap('Set1') +cmap = matplotlib.colormaps["Set1"] fmap = folium.Map(location=[60, -98], - zoom_start=3, - tiles='Stamen Terrain') + zoom_start=3) with urllib.request.urlopen(req) as response: regions_tmp = json.loads(response.read().decode('utf-8')) folium.GeoJson(regions_tmp, @@ -411,6 +412,7 @@ def download_cached_forecasts(): warnings.warn(f"'File $f exists and is larger than version in cache. Not replacing.") else : z.extract(f) + print("Downloaded and extracted", f) download_cached_forecasts() ``` @@ -443,7 +445,7 @@ def get_forecasts(start, end, region): #print("working on {}, {}".format(region,day)) forecasts = forecasts + [get_forecast(day, region)] #print("sleeping") - time.sleep(0.1) # to avoid too much load on Avalanche Canada servers + time.sleep(0.05) # to avoid too much load on Avalanche Canada servers day = day + pd.Timedelta(1,"D") return(forecasts) @@ -481,7 +483,7 @@ for year in range(2011,2019): forecasts = pd.DataFrame.from_dict([f for f in forecastlist if not f==None],orient="columns") forecasts["danger_date"] = forecasts.dangerRatings.apply(lambda r: r[0]["date"]) -forecasts["danger_date"] = pd.to_datetime(forecasts.danger_date, utc=True).dt.date +forecasts["danger_date"] = pd.to_datetime(forecasts.danger_date, format='ISO8601').dt.date forecasts["danger_alpine"]=forecasts.dangerRatings.apply(lambda r: r[0]["dangerRating"]["alp"]) forecasts["danger_treeline"]=forecasts.dangerRatings.apply(lambda r: r[0]["dangerRating"]["tln"]) forecasts["danger_belowtree"]=forecasts.dangerRatings.apply(lambda r: r[0]["dangerRating"]["btl"]) @@ -532,6 +534,7 @@ import nltk import string nltk.download('omw-1.4') nltk.download('punkt') +nltk.download('punkt_tab') nltk.download('stopwords') nltk.download('wordnet') # Remove stopwords (the, a, is, etc) @@ -783,7 +786,7 @@ dimensional space or that the t-SNE algorithm parameters were chosen poorly. ```{code-cell} python -cmap = matplotlib.cm.get_cmap('Paired') +cmap = matplotlib.colormaps["Paired"] fig, ax = plt.subplots(1,2,figsize=(16,6)) n_topics=len(svd_model.components_) lsa_keys = np.argmax(lsa_topic_sample, axis=1) diff --git a/lectures/pandas/basics.md b/lectures/pandas/basics.md index 0249abeb..1b4b9173 100644 --- a/lectures/pandas/basics.md +++ b/lectures/pandas/basics.md @@ -23,7 +23,7 @@ kernelspec: - Use built-in Series transformation functions and be able to create your own and apply them using `apply` - Use built-in scalar transformation functions and be able to create your - own and apply them using `applymap` + own and apply them using `map` - Be able to select subsets of the DataFrame using boolean selection - Know what the "want operator" is and how to apply it @@ -335,7 +335,7 @@ pandas data. To do this, we use the following pattern: 1. Define a Python function that takes in a scalar and produces a scalar. -1. Pass this function as an argument to the `applymap` Series or DataFrame method. +1. Pass this function as an argument to the `map` Series or DataFrame method. Complete the exercise below to practice writing and using your own scalar transforms. @@ -593,7 +593,7 @@ medium (4.5 < x <= 6.5), or low (<= 4.5) for each state and each month. 1. Write a Python function that takes a single number as an input and outputs a single string noting if that number is high, medium, or low. -1. Pass your function to `applymap` (quiz: why `applymap` and not +1. Pass your function to `map` (quiz: why `map` and not `agg` or `apply`?) and save the result in a new DataFrame called `unemp_bins`. 1. (Challenging) This exercise has multiple parts: @@ -617,8 +617,8 @@ medium (4.5 < x <= 6.5), or low (<= 4.5) for each state and each month. ``` ```{code-cell} python -# Part 2: Pass your function from part 1 to applymap -unemp_bins = unemp.applymap#replace this comment with your code!! +# Part 2: Pass your function from part 1 to map +unemp_bins = unemp.map#replace this comment with your code!! ``` ```{code-cell} python diff --git a/lectures/pandas/groupby.md b/lectures/pandas/groupby.md index 6da3a245..fb3f249c 100644 --- a/lectures/pandas/groupby.md +++ b/lectures/pandas/groupby.md @@ -249,7 +249,7 @@ index and a `Date` column added. ```{code-cell} python df2 = df.copy() df2["Date"] = pd.date_range( - start=pd.datetime.today().strftime("%m/%d/%Y"), + start=pd.Timestamp.today().strftime("%m/%d/%Y"), freq="BQ", periods=df.shape[0] ) diff --git a/lectures/pandas/merge.md b/lectures/pandas/merge.md index 96a141c7..430b4dd6 100644 --- a/lectures/pandas/merge.md +++ b/lectures/pandas/merge.md @@ -477,11 +477,13 @@ a future lecture. ```{code-cell} python users_by_n = ( ratings["user_id"] - .value_counts() # Series. Index: user_id, value: n ratings by user - .value_counts() # Series. Index: n_ratings by user, value: N_users with this many ratings + .value_counts() # Series called "count". Index: user_id, value: n ratings by user + .rename("N_ratings") # Rename the Series to "N_ratings" + .value_counts() # Series called "count". Index: n_ratings by user, value: N_users with this many ratings .sort_index() # Sort our Series by the index (number of ratings) .reset_index() # Dataframe with columns `index` (from above) and `user_id` - .rename(columns={"index": "N_ratings", "user_id": "N_users"}) + .rename(columns={"count": "N_users"}) + .set_index("N_ratings") ) users_by_n.head(10) ``` diff --git a/lectures/pandas/the_index.md b/lectures/pandas/the_index.md index 776a2b01..8d9e989b 100644 --- a/lectures/pandas/the_index.md +++ b/lectures/pandas/the_index.md @@ -186,7 +186,7 @@ This would be helpful, for example, if we wanted to compute the difference in the average of all our variables from one year to the next. ```{code-cell} python -df_year.loc[2009].mean() - df_year.loc[2008].mean() +df_year.loc[2009].mean(numeric_only=True) - df_year.loc[2008].mean(numeric_only=True) ``` Notice that pandas did a few things for us. diff --git a/lectures/pandas/timeseries.md b/lectures/pandas/timeseries.md index 4ba57163..9a434a68 100644 --- a/lectures/pandas/timeseries.md +++ b/lectures/pandas/timeseries.md @@ -11,6 +11,9 @@ kernelspec: # Time series +**Co-author** +> - [Philip Solimine, *UBC*](https://www.psolimine.net) + **Prerequisites** - {doc}`Python functions <../python_fundamentals/functions>` @@ -36,12 +39,13 @@ kernelspec: ```{code-cell} python import os +# see section on API keys at end of lecture! import pandas as pd import matplotlib.pyplot as plt -import quandl +import nasdaqdatalink as ndl + +ndl.ApiConfig.api_key = os.environ.get("NASDAQ_DATA_LINK_API_KEY", "jEKP58z7JaX6utPkkpEp") -# see section on API keys at end of lecture! -quandl.ApiConfig.api_key = os.environ.get("QUANDL_AUTH", "Dn6BtVoBhzuKTuyo6hbp") start_date = "2014-05-01" %matplotlib inline @@ -197,17 +201,27 @@ The flexibility of these features is best understood through example, so let's load up some data and take a look. ```{code-cell} python -btc_usd = quandl.get("BCHARTS/BITSTAMPUSD", start_date=start_date) -btc_usd.info() -btc_usd.head() +btc_usd_long = ndl.get_table("QDL/BCHAIN", date = { 'gte': '2009-12-25', 'lte': '2019-01-01' }, code = ["MKPRU", "MKTCP", "ETRVU"]) +btc_usd_long.info() +btc_usd_long.head() ``` Here, we have the Bitcoin (BTC) to US dollar (USD) exchange rate from -March 2014 until today. +2009 until today, as well as other variables relevant to the Bitcoin ecosystem, in long ("melted") form. + +```{code-cell} python +print(btc_usd_long.code.unique()) +btc_usd_long.dtypes +``` + +Notice that the type of `date` is `datetime`. We would like this to be the index, and we want to drop the long form. We also selected only a couple of columns of interest, but the dataset has a lot more options. (The column descriptions can be found [here](https://data.nasdaq.com/databases/BCHAIN)). We chose Market Price (in USD) (`MKPRU`), Total Market Cap (`MKTCP`), and Estimated Transaction Volume in USD (`ETRVU`). -Notice that the type of index is `DateTimeIndex`. +```{code-cell} python +btc_usd = btc_usd_long.pivot_table(index='date', columns='code', values='value') +btc_usd.head() +``` -This is the key that enables things like... +Now that we have a datetime index, it enables things like... Extracting all data for the year 2015 by passing `"2015"` to `.loc`. @@ -289,11 +303,11 @@ btc_date_column.head() ``` ```{code-cell} python -btc_date_column["Date"].dt.year.head() +btc_date_column["date"].dt.year.head() ``` ```{code-cell} python -btc_date_column["Date"].dt.month.head() +btc_date_column["date"].dt.month.head() ``` ## Leads and Lags: `df.shift` @@ -379,8 +393,8 @@ window for the whole dataset. ```{code-cell} python fig, ax = plt.subplots(figsize=(10, 4)) -btc_usd["Open"].plot(ax=ax, linestyle="--", alpha=0.8) -btc_usd.rolling("21d").max()["Open"].plot(ax=ax, alpha=0.8, linewidth=3) +btc_usd["MKPRU"].plot(ax=ax, linestyle="--", alpha=0.8) +btc_usd.rolling("21d").max()["MKPRU"].plot(ax=ax, alpha=0.8, linewidth=3) ax.legend(["Original", "21 day max"]) ``` @@ -471,18 +485,18 @@ See exercise 8 in the {ref}`exercise list `. Recall above that we had the line of code: ```{code-block} python -quandl.ApiConfig.api_key = "Dn6BtVoBhzuKTuyo6hbp" +ndl.ApiConfig.api_key = os.environ.get("NASDAQ_DATA_LINK_API_KEY", "jEKP58z7JaX6utPkkpEp") ``` -This line told the `quandl` library that when obtaining making requests for data, it should use the *API key* `Dn6BtVoBhzuKTuyo6hbp`. +This line told the `nasdaqdatalink` library that when obtaining making requests for data, it should use the *API key* `jEKP58z7JaX6utPkkpEp`. -An API key is a sort of password that web services (like the Quandl API) require you to provide when you make requests. +An API key is a sort of password that web services (like the Nasdaq Data Link API) require you to provide when you make requests. -Using this password, we were able to make a request to Quandl to obtain data directly from them. +Using this password, we were able to make a request to Nasdaq data link to obtain data directly from them. -The API key used here is one that we requested on behalf of this course. +The API key used here is one that we requested on behalf of this course. If you create your own API key, you should store it in the NASDAQ_DATA_LINK_API_KEY environment variable, locally on your computer. Using an environment variable like this is a common way to store sensitive information like API keys, since you can set the environment variable in a secure way that is not stored in your code. How to set environment variables varies by operating system, but you can find instructions for doing so on the web. -If you plan to use Quandl more extensively, you should obtain your own personal API key from [their website](https://docs.quandl.com/docs#section-authentication) and re-run the `quandl.ApiConfig.api_key...` line of code with your new API key on the right-hand side. +If you plan to use Nasdaq data more extensively, you should obtain your own personal API key from [their website](https://www.nasdaq.com/nasdaq-data-link) and re-run the `os.environ...` line of code with your new API key on the right-hand side. (pd-tim-ex)= ## Exercises diff --git a/lectures/problem_sets/problem_set_8.md b/lectures/problem_sets/problem_set_8.md index aadad046..6307caa6 100644 --- a/lectures/problem_sets/problem_set_8.md +++ b/lectures/problem_sets/problem_set_8.md @@ -46,7 +46,7 @@ ahs.info() ```{code-cell} python # dataframe of variable descriptions ahs_doc = pd.read_csv("https://datascience.quantecon.org/assets/data/ahs-doc.csv", encoding="latin1") -with pd.option_context('display.max_rows', None, 'display.max_columns', None, 'max_colwidth', -1): +with pd.option_context('display.max_rows', None, 'display.max_columns', None, 'max_colwidth', None): display(ahs_doc[["Variable","Question","Description","Associated.Response.Codes"]]) ``` diff --git a/lectures/tools/maps.md b/lectures/tools/maps.md index fd354028..359cbf5c 100644 --- a/lectures/tools/maps.md +++ b/lectures/tools/maps.md @@ -125,24 +125,24 @@ that we use here. The file provides the outlines of countries, over which we'll plot the city locations from our GeoDataFrame. -Luckily, `geopandas` already comes bundled with this data, so we don't -have to hunt it down! +Luckily, Natural Earth has already done the hard work of creating these files, and we can pull them directly from their website. ```{code-cell} python -# Grab low resolution world file -world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")) -world = world.set_index("iso_a3") - +# Grab low resolution world file from NACIS +url = "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip" +world = gpd.read_file(url)[['SOV_A3', 'POP_EST', 'CONTINENT', 'NAME', 'GDP_MD', 'geometry']] +world = world.set_index("SOV_A3") +print(world.columns) world.head() ``` `world` is a GeoDataFrame with the following columns: -* `pop_est`: Contains a population estimate for the country -* `continent`: The country's continent -* `name`: The country's name -* `iso_a3`: The country's 3 letter abbreviation (we made this the index) -* `gdp_md_est`: An estimate of country's GDP +* `POP_EST`: Contains a population estimate for the country +* `CONTINENT`: The country's continent +* `NAME`: The country's name +* `SOV_A3`: The country's 3 letter abbreviation (we made this the index) +* `GDP_MD`: The most recent estimate of country's GDP * `geometry`: A `POLYGON` for each country (we will learn more about these soon) ```{code-cell} python @@ -201,7 +201,7 @@ This is a more complex shape than Albania and thus required more points. fig, gax = plt.subplots(figsize=(10,10)) # By only plotting rows in which the continent is 'South America' we only plot SA. -world.query("continent == 'South America'").plot(ax=gax, edgecolor='black',color='white') +world.query("CONTINENT == 'South America'").plot(ax=gax, edgecolor='black',color='white') # By the way, if you haven't read the book 'longitude' by Dava Sobel, you should... gax.set_xlabel('longitude') @@ -234,7 +234,7 @@ fig, gax = plt.subplots(figsize=(10,10)) # By only plotting rows in which the continent is 'South America' we only plot, well, # South America. -world.query("continent == 'South America'").plot(ax = gax, edgecolor='black', color='white') +world.query("CONTINENT == 'South America'").plot(ax = gax, edgecolor='black', color='white') # This plot the cities. It's the same syntax, but we are plotting from a different GeoDataFrame. # I want the cities as pale red dots. @@ -260,7 +260,7 @@ Finally, we might want to consider annotating the cities so we know which cities fig, gax = plt.subplots(figsize=(10,10)) # By only plotting rows in which the continent is 'South America' we only plot, well, South America. -world.query("continent == 'South America'").plot(ax = gax, edgecolor='black', color='white') +world.query("CONTINENT == 'South America'").plot(ax = gax, edgecolor='black', color='white') # This plot the cities. It's the same syntax, but we are plotting from a different GeoDataFrame. I want the # cities as pale red dots. diff --git a/lectures/tools/matplotlib.md b/lectures/tools/matplotlib.md index dd13b6ce..c429c016 100644 --- a/lectures/tools/matplotlib.md +++ b/lectures/tools/matplotlib.md @@ -11,6 +11,9 @@ kernelspec: # Intermediate Plotting +**Co-author** +> - [Philip Solimine, *UBC*](https://www.psolimine.net) + **Prerequisites** - {doc}`Introduction <../pandas/intro>` @@ -35,15 +38,14 @@ kernelspec: ```{code-cell} python import os +os.environ["NASDAQ_DATA_LINK_API_KEY"] = "jEKP58z7JaX6utPkkpEp" + import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib import matplotlib.transforms as transforms -import quandl - -quandl.ApiConfig.api_key = os.environ.get("QUANDL_AUTH", "Dn6BtVoBhzuKTuyo6hbp") - +import nasdaqdatalink as ndl %matplotlib inline ``` @@ -122,7 +124,8 @@ Then, let's grab Apple's stock price data from quandl, starting a few weeks before the first announcement. ```{code-cell} python -aapl = quandl.get("WIKI/AAPL", start_date="2006-12-25") +aapl = ndl.get_table('WIKI/PRICES', ticker = ['AAPL'], date = { 'gte': '2006-12-25', 'lte': '2018-01-01' }) +aapl = aapl.set_index("date") aapl.head() ``` @@ -158,7 +161,7 @@ Let's see some examples. ```{code-cell} python # plot the Adjusted open to account for stock split -ax = aapl["Adj. Open"].plot() +ax = aapl["adj_open"].plot() # get the figure so we can re-display the plot after making changes fig = ax.get_figure() @@ -202,8 +205,8 @@ We can plot from our DataFrame directly on our Axes objects by setting the `ax` argument when calling `.plot`. ```{code-cell} python -aapl[["Adj. Low", "Adj. High"]].plot(ax=axs2[0]) -aapl[["Low", "High"]].plot(ax=axs2[1]) +aapl[["adj_low", "adj_high"]].plot(ax=axs2[0]) +aapl[["low", "high"]].plot(ax=axs2[1]) fig2 ``` @@ -302,7 +305,7 @@ def scale_by_middle(df): # Divide by middle row and scale to 100 # Note: N // 2 is modulus division meaning that it is # rounded to nearest whole number) - out = (df["Open"] / df.iloc[N // 2]["Open"]) * 100 + out = (df["open"] / df.iloc[N // 2]["open"]) * 100 # We don't want to keep actual dates, but rather the number # of days before or after the announcment. 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