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WIP: cambios de la clase de ayer #54

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WIP: cambios de la clase de ayer #54

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29 changes: 26 additions & 3 deletions clases/01_introduccion/pandas_intro.py
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Original file line number Diff line number Diff line change
Expand Up @@ -168,13 +168,15 @@ def rate_height(height):

df['Skin color'].replace({"-": None}, inplace=True)

df[df["Skin color"] != "-"]

df['Skin color']

# ## Eliminar filas con nulos

df

df = df.dropna(subset=['Skin color'])
df = df.dropna(subset=['Skin color'], )
df

# # Unir información de distintas tablas
Expand All @@ -185,13 +187,22 @@ def rate_height(height):

# Tenemos duplicados!

df.merge(df, left_on='Skin color', right_on='Skin color')[['Race_x', 'Race_y']]
# +
df2 = df.copy()

df.merge(df2, left_on='Skin color', right_on='Skin color', )[['Race_x', 'Race_y']]
# -

df1 = pd.DataFrame({"a":[2,2,3], "b":[4,5,6]})
df2 = pd.DataFrame({"c":[2,3,4], "b":[11,12,13]})
df1.merge(df2, left_on="a", right_on="c", how="outer")
df1.merge(df2, left_index=True, right_index=True, how="outer")

# Tenemos que sacar los que son iguales en ambas columnas!

same_skin_color = df.merge(df, left_on='Skin color', right_on='Skin color')[
['Race_x', 'Race_y']
].drop_duplicates()
]#.drop_duplicates()
same_skin_color[same_skin_color.Race_x != same_skin_color.Race_y]

# +
Expand Down Expand Up @@ -235,6 +246,8 @@ def rate_height(height):
df_1 = pd.DataFrame({'col_1': range(1, 10), 'col_2': range(1, 10)})
df_2 = pd.DataFrame({'col_1': range(11, 20), 'col_2': range(11, 20)})

df_2

df_1.pipe(len)

df_2.pipe(len)
Expand All @@ -259,6 +272,8 @@ def rate_height(height):

df.groupby("Race")

callable(list)

df.groupby("Race").agg(list)

(df['Alignment'] == 'good').mean() * 100
Expand Down Expand Up @@ -299,6 +314,8 @@ def perc_good(grouping):
over5 = df.Race.value_counts(normalize=True) > 0.05
mutants_over5 = df.Race.value_counts()[over5]

mutants_over5

# Teniendo la indexacion, veamos como resolverlo con `isin`

df[df.Race.isin(mutants_over5.index)].head(5)
Expand All @@ -322,6 +339,7 @@ def perc_good(grouping):
},
)

# + [markdown] jp-MarkdownHeadingCollapsed=true tags=[]
# # Checkpoint
#
# Desde el siguiente snippet:
Expand All @@ -332,6 +350,7 @@ def perc_good(grouping):
# ```
#
# Encontrar el promedio de altura por raza, considerando solo los personajes _buenos_.
# -

# # Sobre vistas y columnas

Expand All @@ -346,6 +365,8 @@ def alignment_to_numeric(alignment):
df_marvel['numeric_alineation'] = df_marvel.Alignment.apply(alignment_to_numeric)
# -

df_marvel

df_marvel = df[df.Publisher == 'Marvel Comics'].copy()

df_marvel.loc[:, 'numeric_alineation'] = df_marvel.Alignment.apply(alignment_to_numeric)
Expand All @@ -366,6 +387,8 @@ def alignment_to_numeric(alignment):

df.name.apply(lambda x: x.lower())

df.name.str.lower()

# Entre [otras](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html)

# # Manejo de fechas
Expand Down