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"\n",
"Columns (16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"\n",
"df = pd.read_csv('../complaints.csv')\n",
"df['Date received'] = pd.to_datetime(df['Date received'])\n",
"\n",
"cols_to_consider = ['Product','Sub-product','Issue','Sub-issue','Consumer complaint narrative','Company public response','Company',\n",
" 'State', 'ZIP code', 'Date received']\n",
"df_new = df[cols_to_consider]\n",
"\n",
"df_new = df_new.dropna()\n",
"\n",
"\n",
"product_map = {'Credit reporting or other personal consumer reports' : 'Credit Reporting',\n",
" 'Credit reporting, credit repair services, or other personal consumer reports' : 'Credit Reporting',\n",
" 'Payday loan, title loan, personal loan, or advance loan' : 'Loans / Mortgage',\n",
" 'Payday loan, title loan, or personal loan' : 'Loans / Mortgage',\n",
" 'Student loan' : 'Loans / Mortgage',\n",
" 'Vehicle loan or lease' : 'Loans / Mortgage',\n",
" 'Debt collection' : 'Debt collection',\n",
" 'Credit card or prepaid card' : 'Credit/Prepaid Card',\n",
" 'Credit card' : 'Credit/Prepaid Card',\n",
" 'Prepaid card' : 'Credit/Prepaid Card',\n",
" 'Mortgage' : 'Loans / Mortgage',\n",
" 'Checking or savings account' : 'Checking or savings account' \n",
" }\n",
"\n",
"df_new.loc[:,'Product'] = df_new['Product'].map(product_map)\n",
"\n",
"\n",
"df_new['complaint length'] = df_new['Consumer complaint narrative'].apply(lambda x : len(x))\n",
"df_new = df_new[df_new['complaint length'] > 20]\n",
"\n",
"complaints_to_exclude = ['See document attached', 'See the attached documents.', 'Incorrect information on my credit report', 'incorrect information on my credit report',\n",
"'please see attached file','Please see documents Attached','Incorrect information on my credit report.', 'Please see attached file', 'see attached',\n",
"'See attached', 'SEE ATTACHED DOCUMENTS', 'See Attached', 'SEE ATTACHMENT', 'SEE ATTACHMENTS', \n",
"'XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX']\n",
"\n",
"df_new = df_new[~df_new['Consumer complaint narrative'].isin(complaints_to_exclude)]"
]
},
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"### 1. Top 5 common products"
]
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"source": [
"# Function to plot top n most common categories, with horizontal bars, colored bars, no legend, using seaborn palettes for colors\n",
"def plot_top_n(df, column, title, n=5, palette_name=None):\n",
" # Generate a color sequence from the seaborn palette\n",
" color_sequence = sns.color_palette(palette_name, n_colors=n).as_hex() if palette_name else None\n",
" \n",
" # Get top n most common values in the specified column\n",
" counts = df[column].value_counts().reset_index()\n",
" counts.columns = [column, 'Count']\n",
" top_n = counts.head(n)\n",
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" # Create a horizontal bar plot with the seaborn color sequence and remove the legend\n",
" fig = px.bar(top_n, y=column, x='Count', title=title, orientation='h', \n",
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" fig.update_layout(showlegend=False)\n",
" \n",
" # Show the plot\n",
" fig.show()\n",
"\n",
"# Example usage with seaborn palettes\n",
"# Plotting top 5 most common products using the 'viridis' palette\n",
"plot_top_n(df_new, 'Product', 'Top 5 Most Common Products', n=5, palette_name=None)"
]
},
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"### 2. Plotting top 5 most common issues."
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},
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{
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"metadata": {},
"source": [
"### 3. Top 5 Issues in Each Product Category\n"
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{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/hq/f_rzh7j51m1ddfp3xw6s1qsr0000gn/T/ipykernel_83151/1607134567.py:11: DeprecationWarning:\n",
"\n",
"DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
"\n"
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"\n",
"# Calculate total issues per product for ordering\n",
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"\n",
"# Sort products by total issues in descending order\n",
"sorted_products = total_issues_per_product.sort_values('TotalIssues', ascending=False)\n",
"\n",
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"\n",
"# Sort top_issues_per_product DataFrame based on TotalIssues column to ensure the plot respects this order\n",
"top_issues_per_product = top_issues_per_product.sort_values(by=['TotalIssues', 'Count'], ascending=[False, False])\n",
"\n",
"# Step 3: Create a vertical stacked bar chart\n",
"fig = px.bar(top_issues_per_product, x='Product', y='Count', color='Issue',\n",
" title='Top 5 Issues in Each Product Category',\n",
" labels={'Count': 'Number of Complaints'}, \n",
" category_orders={'Product': sorted_products['Product'].tolist()}) # Explicitly set the order of products\n",
"\n",
"# Update layout to remove legend and adjust dimensions for clarity\n",
"fig.update_layout(showlegend=False, width=900, height=600)\n",
"\n",
"# Display the plot\n",
"fig.show()"
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"### 4. Companies with the Most Complaints in 2023"
]
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" orientation='h',\n",
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" color_continuous_scale=[(0.0, \"green\"),\n",
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" (1.0, \"red\")], # This is an example of a nice color scale\n",
" title=f'Top {top_n} Companies with the Most Complaints in 2023',\n",
" labels={'x': 'Number of Complaints', 'y': 'Company'}\n",
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"### 5. Top 10 states with most complaints"
]
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"import pandas as pd\n",
"import plotly.express as px\n",
"\n",
"# State abbreviation to full name mapping\n",
"state_mapping = {\n",
" 'FL': 'Florida', 'CA': 'California', 'TX': 'Texas', 'GA': 'Georgia',\n",
" 'NY': 'New York', 'IL': 'Illinois', 'PA': 'Pennsylvania', 'NC': 'North Carolina',\n",
" 'NJ': 'New Jersey', 'MD': 'Maryland', 'VA': 'Virginia', 'OH': 'Ohio',\n",
" 'MI': 'Michigan', 'SC': 'South Carolina', 'AZ': 'Arizona', 'TN': 'Tennessee',\n",
" 'NV': 'Nevada', 'LA': 'Louisiana', 'AL': 'Alabama', 'MO': 'Missouri',\n",
" 'MA': 'Massachusetts', 'IN': 'Indiana', 'AR': 'Arkansas', 'WA': 'Washington',\n",
" 'CO': 'Colorado', 'MS': 'Mississippi', 'CT': 'Connecticut', 'MN': 'Minnesota',\n",
" 'WI': 'Wisconsin', 'KY': 'Kentucky', 'UT': 'Utah', 'DE': 'Delaware',\n",
" 'OR': 'Oregon', 'OK': 'Oklahoma', 'DC': 'District of Columbia', 'KS': 'Kansas',\n",
" 'IA': 'Iowa', 'NM': 'New Mexico', 'NE': 'Nebraska', 'HI': 'Hawaii',\n",
" 'RI': 'Rhode Island', 'ID': 'Idaho', 'WV': 'West Virginia', 'NH': 'New Hampshire',\n",
" 'ME': 'Maine', 'MT': 'Montana', 'ND': 'North Dakota', 'AK': 'Alaska',\n",
" 'SD': 'South Dakota', 'WY': 'Wyoming', 'VT': 'Vermont'\n",
" # Removed territories and minor outlying islands not listed as states\n",
"}\n",
"\n",
"# Assuming df_new is your DataFrame and 'State' contains the abbreviations\n",
"# Map state abbreviations to full names\n",
"df_new['State Name'] = df_new['State'].map(state_mapping)\n",
"\n",
"# Calculate complaint counts by state\n",
"state_complaint_counts = df_new['State Name'].value_counts()\n",
"\n",
"# Get top 10 states with the most complaint counts\n",
"top_n = 10\n",
"top_states = state_complaint_counts.head(top_n)\n",
"\n",
"# Create a horizontal bar chart using Plotly Express with a nice color scale\n",
"fig = px.bar(\n",
" y=top_states.values,\n",
" x=top_states.index,\n",
" orientation='v',\n",
" color=top_states.values, # Assign color based on values\n",
" color_continuous_scale='Turbo', # A nice color scale\n",
" title=f'Top {top_n} States with the Most Complaints',\n",
" labels={'y': 'Number of Complaints', 'x': 'State'},\n",
" category_orders={'y': top_states.index.tolist()}\n",
")\n",
"\n",
"fig.update_layout(\n",
" yaxis=dict(\n",
" tickfont=dict(size=10),\n",
" ),\n",
" xaxis=dict(\n",
" tickangle=0,\n",
" ),\n",
" height=600,\n",
" width=1000,\n",
")\n",
"\n",
"# To display a color bar, showing the mapping of colors to values\n",
"fig.update_layout(coloraxis_showscale=False)\n",
"\n",
"fig.show()\n"
]
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"source": [
"### 6. Top 10 states with least complaints"
]
},
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"\n",
"# State abbreviation to full name mapping\n",
"state_mapping = {\n",
" 'FL': 'Florida', 'CA': 'California', 'TX': 'Texas', 'GA': 'Georgia',\n",
" 'NY': 'New York', 'IL': 'Illinois', 'PA': 'Pennsylvania', 'NC': 'North Carolina',\n",
" 'NJ': 'New Jersey', 'MD': 'Maryland', 'VA': 'Virginia', 'OH': 'Ohio',\n",
" 'MI': 'Michigan', 'SC': 'South Carolina', 'AZ': 'Arizona', 'TN': 'Tennessee',\n",
" 'NV': 'Nevada', 'LA': 'Louisiana', 'AL': 'Alabama', 'MO': 'Missouri',\n",
" 'MA': 'Massachusetts', 'IN': 'Indiana', 'AR': 'Arkansas', 'WA': 'Washington',\n",
" 'CO': 'Colorado', 'MS': 'Mississippi', 'CT': 'Connecticut', 'MN': 'Minnesota',\n",
" 'WI': 'Wisconsin', 'KY': 'Kentucky', 'UT': 'Utah', 'DE': 'Delaware',\n",
" 'OR': 'Oregon', 'OK': 'Oklahoma', 'DC': 'District of Columbia', 'KS': 'Kansas',\n",
" 'IA': 'Iowa', 'NM': 'New Mexico', 'NE': 'Nebraska', 'HI': 'Hawaii',\n",
" 'RI': 'Rhode Island', 'ID': 'Idaho', 'WV': 'West Virginia', 'NH': 'New Hampshire',\n",
" 'ME': 'Maine', 'MT': 'Montana', 'ND': 'North Dakota', 'AK': 'Alaska',\n",
" 'SD': 'South Dakota', 'WY': 'Wyoming', 'VT': 'Vermont'\n",
" # Removed territories and minor outlying islands not listed as states\n",
"}\n",
"\n",
"# Assuming df_new is your DataFrame and 'State' contains the abbreviations\n",
"# Map state abbreviations to full names\n",
"df_new['State Name'] = df_new['State'].map(state_mapping)\n",
"\n",
"# Calculate complaint counts by state\n",
"state_complaint_counts = df_new['State Name'].value_counts()\n",
"\n",
"# Get top 10 states with the most complaint counts\n",
"top_n = 10\n",
"top_states = state_complaint_counts.tail(top_n)\n",
"\n",
"# Create a horizontal bar chart using Plotly Express with a nice color scale\n",
"fig = px.bar(\n",
" y=top_states.values,\n",
" x=top_states.index,\n",
" orientation='v',\n",
" color=top_states.values, # Assign color based on values\n",
" color_continuous_scale='Turbo', # A nice color scale\n",
" title=f'Top {top_n} States with the Most Complaints',\n",
" labels={'y': 'Number of Complaints', 'x': 'State'},\n",
" category_orders={'y': top_states.index.tolist()}\n",
")\n",
"\n",
"fig.update_layout(\n",
" yaxis=dict(\n",
" tickfont=dict(size=10),\n",
" ),\n",
" xaxis=dict(\n",
" tickangle=0,\n",
" ),\n",
" height=600,\n",
" width=1050,\n",
")\n",
"\n",
"# To display a color bar, showing the mapping of colors to values\n",
"fig.update_layout(coloraxis_showscale=False)\n",
"\n",
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"source": [
"### 6. Complaints across states"
]
},
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"df_2023 = df_new[df_new['Date received'].dt.year == 2023]\n",
"\n",
"state_complaints = df_2023.groupby('State').size().reset_index(name='Num_complaints')\n",
"#state_complaints['text'] = 'Total Complaints in 2023: ' + state_complaints['Num_complaints'].astype(str)\n",
"state_complaints['Full_state_name'] = state_complaints['State'].apply(lambda x : state_mapping[x] if x in state_mapping else x)\n",
"\n",
"fig = px.choropleth(state_complaints,\n",
" locations='State',\n",
" locationmode='USA-states',\n",
" color='Num_complaints',\n",
" color_continuous_scale='Inferno',\n",
" scope=\"usa\",\n",
" hover_name='Full_state_name', \n",
" #hover_data={'State': False, 'Num_complaints': True}, \n",
" title='Number of Complaints by State')\n",
"fig.add_scattergeo(\n",
" locations=state_complaints['State'], ###codes for states,\n",
" locationmode='USA-states',\n",
" text=state_complaints['State'],\n",
" mode='text',\n",
" hoverinfo='skip',\n",
" textfont=dict(size = 8.5,color='white'))\n",
"\n",
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{
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"metadata": {},
"source": [
"### 7. Evolution of complaints across the years"
]
},
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"monthly_complaints = df_new.copy()\n",
"monthly_complaints = monthly_complaints[monthly_complaints['Date received'].dt.year != 2024]\n",
"\n",
"monthly_complaints['MonthYear'] = monthly_complaints['Date received'].dt.to_period('M').astype(str)\n",
"monthly_complaints = monthly_complaints.groupby('MonthYear').size().reset_index(name = \"NumComplaints\")\n",
"\n",
"\n",
"fig = px.line(monthly_complaints, x='MonthYear', y='NumComplaints',\n",
" labels={'MonthYear': 'Year', 'NumComplaints': 'Number of Complaints'})\n",
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"fig.update_layout(\n",
" title={\n",
" 'y':0.9,\n",
" 'x':0.5,\n",
" 'xanchor': 'center',\n",
" 'yanchor': 'top'\n",
" },\n",
" width=1000,\n",
" height=500\n",
" )\n",
" \n",
"fig.show()"
]
}
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