import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/rasbt/python_reference/master/Data/some_soccer_data.csv')
df

# Converting column names to lowercase

df.columns = [c.lower() for c in df.columns]

# or
# df.rename(columns=lambda x : x.lower())

df.tail(3)

df = df.rename(columns={'p': 'points', 
                        'gp': 'games',
                        'sot': 'shots_on_target',
                        'g': 'goals',
                        'ppg': 'points_per_game',
                        'a': 'assists',})

df.tail(3)

# Processing `salary` column

df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))
df.tail()

df['team'] = pd.Series('', index=df.index)

# or
df.insert(loc=8, column='position', value='') 

df.tail(3)

# Processing `player` column

def process_player_col(text):
    name, rest = text.split('\n')
    position, team = [x.strip() for x in rest.split(' — ')]
    return pd.Series([name, team, position])

df[['player', 'team', 'position']] = df.player.apply(process_player_col)

# modified after tip from reddit.com/user/hharison
#
# Alternative (inferior) approach:
#
#for idx,row in df.iterrows():
#    name, position, team = process_player_col(row['player'])
#    df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team
    
df.tail(3)

cols = ['player', 'position', 'team']
df[cols] = df[cols].applymap(lambda x: x.lower())
df.head()

nans = df.shape[0] - df.dropna().shape[0]

print('%d rows have missing values' % nans)

# Selecting all rows that have NaNs in the `assists` column

df[df['assists'].isnull()]

df[df['assists'].notnull()]

# Filling NaN cells with default value 0

df.fillna(value=0, inplace=True)
df

# Adding an "empty" row to the DataFrame

import numpy as np

df = df.append(pd.Series(
                [np.nan]*len(df.columns), # Fill cells with NaNs
                index=df.columns),    
                ignore_index=True)

df.tail(3)

# Filling cells with data

df.loc[df.index[-1], 'player'] = 'new player'
df.loc[df.index[-1], 'salary'] = 12.3
df.tail(3)

# Sorting the DataFrame by a certain column (from highest to lowest)

df.sort_values('goals', ascending=False, inplace=True)
df.head()

# Optional reindexing of the DataFrame after sorting

df.index = range(1,len(df.index)+1)
df.head()

# Creating a dummy DataFrame with changes in the `salary` column

df_2 = df.copy()
df_2.loc[0:2, 'salary'] = [20.0, 15.0]
df_2.head(3)

# Temporarily use the `player` columns as indices to 
# apply the update functions

df.set_index('player', inplace=True)
df_2.set_index('player', inplace=True)
df.head(3)

# Update the `salary` column
df.update(other=df_2['salary'], overwrite=True)
df.head(3)

# Reset the indices
df.reset_index(inplace=True)
df.head(3)

# Selecting only those players that either playing for Arsenal or Chelsea

df[ (df['team'] == 'arsenal') | (df['team'] == 'chelsea') ]

# Selecting forwards from Arsenal only

df[ (df['team'] == 'arsenal') & (df['position'] == 'forward') ]

types = df.columns.to_series().groupby(df.dtypes).groups
types

# select string columns
df.loc[:, (df.dtypes == np.dtype('O')).values].head()

types = df.columns.to_series().groupby(df.dtypes).groups
types

int(True)

import pandas as pd

a = [[2., .3, 4., 5.], [.8, .03, 0.02, 5.]]
df = pd.DataFrame(a)
df

df = df <= 0.05
df

df.astype(int)