def ts_gradient(dfg, type_trans, avg_month):
"""
Creates a timeseries plot where all years are ploted simultaneously
Args:
dfg: dataframe to use
type_trans: type of transaction [Income/Expense]
avg_month: month to use in time average
Returns:
the plotly plot as html-div format
"""
if type_trans in [c.names.INCOMES, c.names.EXPENSES]:
df = dfg[dfg[c.cols.TYPE] == type_trans].copy()
else:
df = dfg.copy()
mfilter = df[c.cols.TYPE] == c.names.EXPENSES
df.loc[mfilter, c.cols.AMOUNT] = -df.loc[mfilter, c.cols.AMOUNT]
df = u.group_df_by(df, "M")
if df.shape[0] == 0:
return {}
df = u.time_average(df, avg_month)
max_width = 5
color_name = {
c.names.INCOMES: "green",
c.names.EXPENSES: "red"
}.get(type_trans, "amber")
data = []
for year in sorted(set(df.index.year), reverse=False):
index_color = max(100, 900 - 200 * (max(df.index.year) - year))
color = u.get_colors((color_name, index_color))
df_aux = df[df.index.year == year]
data.append(
go.Scatter(
x=df_aux.index.month,
y=df_aux[c.cols.AMOUNT].values,
line={
"width": min(0.5 * (year - min(df.index.year)) + 1,
max_width)
},
marker={"color": color},
name=year,
mode="lines",
))
layout = go.Layout(title="Time comparison ({})".format(type_trans),
hovermode="closest")
return go.Figure(data=data, layout=layout)
def dist_plot(dfg):
"""
Creates a distribution plot with expenses, incomes and ebit
Args:
dfg: dataframe to use
Returns:
the plotly plot as html-div format
"""
dfe = u.group_df_by(dfg[dfg[c.cols.TYPE] == c.names.EXPENSES], "M")
dfi = u.group_df_by(dfg[dfg[c.cols.TYPE] == c.names.INCOMES], "M")
df_baii = dfi - dfe
iter_data = [
(df_baii, c.names.EBIT, c.colors.EBIT),
(dfe, c.names.EXPENSES, c.colors.EXPENSES),
(dfi, c.names.INCOMES, c.colors.INCOMES),
]
# Generate traces to show. This allow to disable traces if there is no data
data, names, colors = [], [], []
for df, name, color in iter_data:
# Some data needed
if (df[c.cols.AMOUNT].sum() > 0) and (len(df[c.cols.AMOUNT].unique()) >
1):
data.append(df[c.cols.AMOUNT].fillna(0).tolist())
names.append(name)
colors.append(color)
if not data:
return {}
fig = FF.create_distplot(data, names, colors=colors, bin_size=100)
fig["layout"].update(title="Incomes, Expenses and EBIT distribution")
return fig
def plot_expenses_vs_liquid(df_liquid_in,
df_trans_in,
avg_month,
show_rec=True,
height=None):
"""
Creates a plot to compare liquid and expenses
Args:
df_liq_in: dataframe with liquid info
df_trans_in: dataframe with transactions
avg_month: month to use in time average
show_rec: bool for show/hide recommended liquids
height: height of the plot
Returns:
the plotly plot as html-div format
"""
df_l = df_liquid_in.set_index(c.cols.DATE).copy()
df_l = u.time_average(df_l.fillna(0), avg_month)
df_t = u.group_df_by(
df_trans_in[df_trans_in[c.cols.TYPE] == c.names.EXPENSES], "M")
df_t = u.time_average(df_t, avg_month)
iter_data = [
(df_t, df_t[c.cols.AMOUNT], c.names.EXPENSES, c.colors.EXPENSES),
(df_l, df_l[c.names.TOTAL], c.names.LIQUID, c.colors.LIQUID),
(df_t, MONTHS_MIN * df_t[c.cols.AMOUNT], c.names.LIQUID_MIN_REC,
c.colors.LIQUID_MIN_REC),
(df_t, MONTHS_REC * df_t[c.cols.AMOUNT], c.names.LIQUID_REC,
c.colors.LIQUID_REC),
]
if not show_rec:
iter_data = iter_data[:2]
data = [
go.Scatter(x=df.index,
y=y,
name=name,
marker={"color": color},
mode="lines") for df, y, name, color in iter_data
]
layout = go.Layout(title="Liquid vs Expenses",
showlegend=show_rec,
height=height)
return go.Figure(data=data, layout=layout)
def plot_months(df_liquid_in,
df_trans_in,
avg_month,
show_rec=True,
height=None):
"""
Creates a plot to compare liquid and expenses
Args:
df_liq_in: dataframe with liquid info
df_trans_in: dataframe with transactions
avg_month: month to use in time average
show_rec: bool for show/hide recommended liquids
height: height of the plot
Returns:
the plotly plot as html-div format
"""
df_l = df_liquid_in.set_index(c.cols.DATE).copy()
df_l = u.time_average(df_l.fillna(0), avg_month)
df_t = u.group_df_by(
df_trans_in[df_trans_in[c.cols.TYPE] == c.names.EXPENSES], "M")
df_t = u.time_average(df_t, avg_month)
serie = df_l[c.names.TOTAL] / df_t[c.cols.AMOUNT]
iter_data = [
(serie, "Months", c.colors.LIQUID),
([MONTHS_MIN] * len(serie), "Minimum months of liquid",
c.colors.LIQUID_MIN_REC),
([MONTHS_REC] * len(serie), "Recommended months of liquid",
c.colors.LIQUID_REC),
]
if not show_rec:
iter_data = iter_data[:1]
data = [
go.Scatter(x=serie.index,
y=y,
name=name,
marker={"color": color},
mode="lines") for y, name, color in iter_data
]
layout = go.Layout(title="Survival months with current liquid",
height=height)
return go.Figure(data=data, layout=layout)
def passive_income_vs_expenses(df_wor_in, df_trans_in, avg_month, smooth):
"""
Compares what can be generated with passive income to expanses
Args:
df_wor_in: dataframe with investment worth
df_trans_in: dataframe with transactions
avg_month: month to use in time average
smooth: bool to allow/disable passive smoothing
Returns:
the plotly plot as html-div format
"""
dfw = df_wor_in.set_index(c.cols.DATE)[[c.names.TOTAL]]
if smooth:
dfw = u.time_average(dfw, avg_month)
dfe = u.group_df_by(
df_trans_in[df_trans_in[c.cols.TYPE] == c.names.EXPENSES], "M")
dfe = u.time_average(dfe, avg_month)
data = [
go.Scatter(
x=dfw.index,
y=dfw[c.names.TOTAL] * 0.04 / 12,
name="Passive income",
marker={"color": c.colors.INCOMES_PASSIVE},
),
go.Scatter(x=dfe.index,
y=dfe[c.cols.AMOUNT],
name="Expenses",
marker={"color": c.colors.EXPENSES}),
]
layout = go.Layout(title="Passive income vs expenses", barmode="stack")
return go.Figure(data=data, layout=layout)
def get_heatmap(dfg, type_trans):
"""
Creates a heatmap with expenses or incomes
Args:
dfg: dataframe to use
type_trans: type of transaction [Income/Expense]
Returns:
the plotly plot as html-div format
"""
df = u.group_df_by(dfg[dfg[c.cols.TYPE] == type_trans], "M")
# No data no fun
if df.shape[0] < 2:
return {}
df[c.cols.YEAR], df[c.cols.MONTH_DATE] = df.index.year, df.index.month
df = df.pivot(c.cols.MONTH_DATE, c.cols.YEAR,
c.cols.AMOUNT).sort_index(ascending=False)
# Fix month names
df.index = [month_abbr[x] for x in df.index]
cmap = {c.names.INCOMES: "Greens", c.names.EXPENSES: "YlOrRd"}[type_trans]
data = go.Heatmap(x=df.columns,
y=df.index,
z=df.values,
colorscale=cmap,
reversescale=True,
showscale=False)
layout = go.Layout(title="Heatmap ({})".format(type_trans))
return go.Figure(data=[data], layout=layout)