<jupyter_start><jupyter_text>Clipping our results above 0<jupyter_code>joint_kws=dict(gridsize=25)
g2 = sns.jointplot(dv['Age'], dv['Squat1Kg'], data=dv, kind="hex", ylim=(0,500) ,dropna=True, joint_kws=joint_kws)

joint_kws=dict(gridsize=23)
g3 = sns.jointplot(dvn['Age'], dvn['Squat1Kg'], data=dvn, kind="hex",ylim=(0,500) ,dropna=True, joint_kws=joint_kws)

g = sns.pairplot(dv, hue='Sex', palette='husl', x_vars=['Age', 'BodyweightKg'], y_vars=lifts, kind="reg")

data_nonzeros = data.query('Best3SquatKg > 0')
data_zeros = data.query('Best3SquatKg == 0')
males = data_nonzeros.query("Sex == 'M'")
females = data_nonzeros.query("Sex == 'F'")
males_0 = data_zeros.query("Sex == 'M'")
females_0 = data_zeros.query("Sex =='F'")
males_all = data.query("Sex == 'M'")
females_all = data.query("Sex == 'F'")
f, ax = subplots(figsize=(9,5))

ax = sns.kdeplot(males['BodyweightKg'], males['Best3SquatKg'], cmap="Blues", shade=True, shade_lowest=False, cbar=True)
ax = sns.kdeplot(females['BodyweightKg'], females['Best3SquatKg'], cmap="Reds", shade=True, shade_lowest=False, cbar=True)

red= sns.color_palette("Reds")[-2]
blue= sns.color_palette("Blues")[-2]

ax.text(100, 350, "Males", size=14, color=blue)
ax.text(100, 100, "Females", size=14, color=red)
f, ax = subplots(figsize=(8,8))

ax = sns.kdeplot(males['BodyweightKg'], males['Best3SquatKg'], cmap="Greens", shade=True, shade_lowest=False, cbar=True)
ax = sns.kdeplot(males_0['BodyweightKg'], males_0['Best3SquatKg'], cmap="Oranges", shade=True, shade_lowest=False, cbar=True)

green= sns.color_palette("Greens")[-2]
orange= sns.color_palette("Oranges")[-2]

ax.text(2.5, 8.2, "Succeded", size=16, color=green)
ax.text(3.8, 4.5, "Failed", size=16, color=orange)
f, ax = subplots(figsize=(8,5))

ax = sns.kdeplot(males['BodyweightKg'], males['Age'], cmap="Oranges", shade=True, shade_lowest=False, cbar=True)

orange= sns.color_palette("Oranges")[-2]

ax.text(150, 20, "Failed", size=14, color=orange)
f, ax = subplots(figsize=(8,5))

ax = sns.kdeplot(females_0['BodyweightKg'], females_0['Age'], cmap="Oranges", shade=True, shade_lowest=False, cbar=True)

orange= sns.color_palette("Oranges")[-2]

ax.text(150, 20, "Failed", size=14, color=orange)
maxed = data['Best3SquatKg'].max() 
bins = np.linspace(0, maxed, 50)
g = sns.FacetGrid(data, col="Sex" , row="EquipmentBool", hue="BestSquat", height=5, aspect=1)
g = (g.map(hist, "Best3SquatKg", bins=bins, alpha=0.5).add_legend())
bins = np.linspace(0, data_nonzeros['Best3SquatKg'].max() , 50)
g = sns.FacetGrid(data_nonzeros, col="EquipmentBool", hue="Sex", height=5, aspect=1)
g = (g.map(hist, "Best3SquatKg", bins=bins, alpha=0.5).add_legend())
g = sns.FacetGrid(df, col="Sex", hue="Sex", legend_out=True, height=5, aspect=1)
g = (g.map(scatter, "BodyweightKg", "Squat1Kg", edgecolor="w").add_legend())
g = sns.FacetGrid(df, col="Sex", hue="Sex", legend_out=True, height=5, aspect=1)
g = (g.map(scatter, "Age", "Squat1Kg", edgecolor="w"))<jupyter_output><empty_output>
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