Indexing by Position
Learn about indexing by position in DataFrames.
We'll cover the following...
We'll cover the following...
The iloc
attribute gives us the ability to pull out rows and columns from a DataFrame. Here we pull out row position 1. Note that this returns the result as a Series (even though it represents a row):
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Python 3.8
def tweak_siena_pres(df):def int64_to_uint8(df_):cols = df_.select_dtypes('int64')return (df_.astype({col:'uint8' for col in cols}))return (df.rename(columns={'Seq.':'Seq'}) # 1.rename(columns={k:v.replace(' ', '_') for k,v in{'Bg': 'Background','PL': 'Party leadership', 'CAb': 'Communication ability','RC': 'Relations with Congress', 'CAp': 'Court appointments','HE': 'Handling of economy', 'L': 'Luck','AC': 'Ability to compromise', 'WR': 'Willing to take risks','EAp': 'Executive appointments', 'OA': 'Overall ability','Im': 'Imagination', 'DA': 'Domestic accomplishments','Int': 'Integrity', 'EAb': 'Executive ability','FPA': 'Foreign policy accomplishments','LA': 'Leadership ability','IQ': 'Intelligence', 'AM': 'Avoid crucial mistakes','EV': "Experts' view", 'O': 'Overall'}.items()}).astype({'Party':'category'}) # 2.pipe(int64_to_uint8) # 3.assign(Average_rank=lambda df_:(df_.select_dtypes('uint8') # 4.sum(axis=1).rank(method='dense').astype('uint8')),Quartile=lambda df_:pd.qcut(df_.Average_rank, 4,labels='1st 2nd 3rd 4th'.split())))import pandas as pdurl = 'https://github.com/mattharrison/datasets/raw/master/data/'\'siena2018-pres.csv'df = pd.read_csv(url, index_col=0)pres = tweak_siena_pres(df)print(pres.iloc[1])
In the next example, instead of passing in scalar position, we’re going to pass in row position 1 in a list. Sometimes we’ll hear people tell ...