date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')

df.describe().transpose()

It’s just that it often makes sense to write code in the order JOIN / WHERE / GROUP BY / HAVING. (I’ll often put a WHERE first to improve performance though, and I think most database engines will also do a WHERE first in practice)

Indicate number of NA values placed in non-numeric columns.

Filled 3 NA values in column name

Tokenization took: 0.01 ms
Type conversion took: 0.70 ms
Parser memory cleanup took: 0.01 ms

Efficient way to loop over Pandas Dataframe to make dummy variables (1 or 0 input)

Measurements are variables that can be quantified. All data in the output above are measurements. Some of these measurements, such as state_percentile_16, avg_score_16 and school_rating, are outcomes; these outcomes cannot be used to explain one another. For example, explaining school_rating as a result of state_percentile_16 (test scores) is circular logic. Therefore we need a second class of variables.

nning. It's time to start annotating som

if you need to pull out these rows and examine them

GeoPandas

I'll skip the inefficient method I used before with the custom groupby aggregationm, and go for some neat trick using the mighty transform method.

gs.resample("5d").mean().head()

df['n_votes'] = df.n_votes.astype(int, errors='ignore')

ax = df['color'].value_counts().plot('bar')