Pandas

Frequently used code for pandas related code snippets

plot columns

df.plot(figsize=(20,10))

fill na

dt.fillna(0)

Reading data from Postgresql

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine("postgresql://user@localhost:5432/mydb")
df = pd.read_sql_query("select * from table", con=engine)

Add rows

dt.append({"col1":[1,2,3], "coln":[4,5,6]})

pivoting dataframe

df.pivot(columns="col_with_columns",values="col_with_values")

getting index of max/min values

df['column'].idxmax()

Count instances of values in column

df['target'].value_counts().sort_values(ascending=False)

Filtering by column's value len

df[df['itemsets'].map(len) > 2]

Iterate by rows

for index, row in df.iterrows():
    print(row['c1'], row['c2'])

Subsetting columns

df[['col1', 'col2']]

or by regex pattern

df.filter(regex=("d.*"))

Parse datetimes

# errors = ['coerce','raise','ignore'] # default: 'raise'
df['date'] = pd.to_datetime(df.date, format='%Y-%m-%d')

Group by dates

df[df.date.dt.day == 8] #(or hour or minute)

Plot evolution over time

%matplotlib inline 
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(18, 13), dpi= 80, facecolor='w', edgecolor='k')

ax = plt.gca()
hist_dt[hist_dt["target_community"]==0].groupby([hist_dt.created_at.dt.day,hist_dt.created_at.dt.hour]).size().plot(ax=ax)
# if multiple samples: hist_dt[hist_dt["target_community"]==1].groupby([hist_dt.created_at.dt.day,hist_dt.created_at.dt.hour]).size().plot(ax=ax)
# if not the subsetting part could be removed
plt.xlabel('Dia,Hora de publicación')
plt.ylabel('Cantidad de tweets')

New column A based on column B

df['Discounted_Price'] = df.apply(lambda row: row.Cost - 
                                  (row.Cost * 0.1), axis = 1)
#if row.property == 1 else None # for conditional setting

To select rows whose column value equals a scalar, some_value, use ==:

df.loc[df['column_name'] == some_value]

To select rows whose column value is in an iterable, some_values, use isin:

df.loc[df['column_name'].isin(some_values)]

To select rows whose column value is in another column array

df.apply(lambda x: x['Responsibility Type'] in x['Roles'], axis=1)

Combine multiple conditions with &:

df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]

Note the parentheses. Due to Python's operator precedence rules, & binds more tightly than <= and >=. Thus, the parentheses in the last example are necessary. Without the parentheses

df['column_name'] >= A & df['column_name'] <= B

is parsed as

df['column_name'] >= (A & df['column_name']) <= B

To select rows whose column value does not equal some_value, use !=:

df.loc[df['column_name'] != some_value]

To select rows whose value is not in some_values

df.loc[~df['column_name'].isin(some_values)]

Improve query efficiency by setting an index

df = df.set_index(['colname'])

Order columns lexicographically

df = df.reindex(sorted(df.columns), axis=1)

Remove duplicates (distinct)

df.drop_duplicates()# subset=["col1", "col2"] use only those columns for distinction

Check if a field exists

df[df['column'].isnull()]
#or
df[df['column'].notnull()]

Replacing column values

w['female'] = w['female'].map({'female': 1, 'male': 0})
#or
dt.loc[dt['ccLeadtime'] == -1, 'ccLeadtime'] = 0

One-hot encoding

dfDummies = pd.get_dummies(df['categorical'], prefix = 'category')
df = pd.concat([df, dfDummies], axis=1)

Rename column

df = df.rename(columns={"A": "a", "B": "c"})

Rename index names

df.index.names = ['new_name']

Remove column from list

df.drop(['pop'], axis=1)

Join dataframes

pd.merge(df1,df2,on='key')

Iterate by groups

grouped = df.groupby('A')
for name, group in grouped:

Group data by year

data.groupby(data.date.dt.year)

Count unique by group

df.groupby('location')['user'].nunique()

Collect list in Group by

df.groupby('a')['b'].apply(list).reset_index(name='new')

Get normalized values from groupby count

df2 = df.groupby(['subset_product', 'subset_close']).size().reset_index(name='prod_count')
a = df2.groupby('subset_product')['prod_count'].transform('sum')
df2['prod_count'] = df2['prod_count'].div(a)

Generate csv from dataframe

df.to_csv('filename.csv',index=False)

Sum to date column

df['date'] = df['created'] + pd.to_timedelta(7, unit='d')

Replace character in column

df["column"] = df["column"].str.replace('foo', 'bar')

References

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