import seaborn as sns import pandas as pd import numpy as np from matplotlib import pyplot as plt

df = pd.read_excel("/work/g2_v.xlsx")

df.dtypes

df["gestion"]=pd.Categorical(df["gestion"])

sns.heatmap(df.corr(), cmap= "Greens", annot=True, linewidths=1, annot_kws = { "fontsize":16})

type(g)

df2 = pd.read_excel("/work/g2.xlsx")

df2["year"]=pd.Categorical(df2["year"])

cars = sns.load_dataset("mpg")

cuadro = cars.groupby("origin").cylinders.value_counts().unstack()

type(cuadro)

dfg1=pd.read_excel("/work/g1v.xlsx")

dfg1.dtypes

!pip install pyperclip==1.8.2

import pyperclip

pip install gtk

df_c = pd.read_clipboard() df_c

pip install xsel

sns.relplot(dfg1, x="gestion", y="indice", hue="indicador")

dfg1

fmri=sns.load_dataset("fmri")

dfeje.dtypes

dfg1.dtypes

df_g1_g3 = pd.read_excel("/work/g1_g3.xlsx")

df_g1_g3

df_g1_g3.columns

sns.relplot(x="gestion", y="indice", hue="depto", style="depto", col="indicador", col_wrap=5, height=3, aspect=.75, linewidth=2.5, kind="line", data=df_g1_g3.query("grupo == 'g2'")) sns.set_context("notebook", font_scale=1.25)

df_g1_g3.dtypes

sns.relplot(x="gestion", y="indice", hue="depto", style="depto", col="indicador", col_wrap=5, height=3.5, aspect=.75, linewidth=2.5, kind="line", data=df_g1_g3.query("grupo == 'g2' and gestion <= 2019"));

data=df_g1_g3.query("grupo == 'g2' and depto == 'La Paz' and gestion <= 2019")

matriztot = data.groupby(["gestion" , "indicador"])["indice"].mean().unstack() matriztot

type(matrizg1)

matrizg3.dtypes

sns.heatmap(matriztot.corr(), cmap= "Greens", annot=True, linewidths=1, annot_kws = { "fontsize":8})

data=df_g1_g3.query("depto == 'La Paz' and gestion <= 2019")

data

sns.heatmap(data.corr(), cmap= "Greens", annot=True, linewidths=1, annot_kws = { "fontsize":15})

df = pd.read_excel("/work/g1_g4.xlsx")

data=df.query("depto == 'La Paz' and gestion <= 2019 and gestion >= 2012")

data

matriztot = data.groupby(["gestion" , "indicador"])["indice"].mean().unstack() matriztot

sns.clustermap(matriztot, figsize=(4,4))

matriztot

sns.heatmap(matriztot.corr(), cmap= "Greens", annot=True, linewidths=1, annot_kws = { "fontsize":5})

data_g1_g2=df.query("grupo == 'g1' or grupo == 'g2' and depto == 'La Paz' and gestion <= 2019")

matriztot_g12 = data_g1_g2.groupby(["gestion" , "indicador"])["indice"].mean().unstack() matriztot_g12

sns.heatmap(matriztot_g12.corr(), cmap= "Greens", annot=True, linewidths=1, annot_kws = { "fontsize":9})

data_g3_g4=df.query("grupo == 'g3' or grupo == 'g4' and depto == 'La Paz' and gestion <= 2019")

matriztot_g34 = data_g3_g4.groupby(["gestion" , "indicador"])["indice"].mean().unstack()

sns.heatmap(matriztot_g34.corr(), cmap= "Greens", annot=True, linewidths=1, annot_kws = { "fontsize":9})

data

matriztot = df.groupby(["gestion" , "indicador"])["indice"].mean().unstack() matriztot

sns.heatmap(matriztot.corr(), cmap= "Greens", annot=True, linewidths=1, annot_kws = { "fontsize":7})

data=df.query("depto == 'La Paz' and gestion <= 2019")

data=df.query("depto == 'La Paz' and gestion <= 2019")

type(data)

data

sns.relplot(x="gestion", y="indice", hue="depto", style="depto", col="indicador", col_wrap=3, height=3.5, aspect=.75, linewidth=2.5, kind="line", data=df.query("grupo == 'g4' and gestion <= 2019"));