In this note, a general dataframe is called
df (type pandas.core.frame.DataFrame), a general series is call s (type pandas.core.series.Series).1import pandas as pd
2import numpy as np # import numpy if necessary1# READ
2df = pd.read_csv('filename.csv', sep=';') # default sep=','
3
4# if 1st col contains 0,1,2,...
5df = pd.read_csv('filename.csv', index_col=1)
6
7# with datetime info
8df = pd.read_csv(PATH_DATA_FOLDER+"raw_data.csv",
9 parse_dates=['timestamp'],
10 infer_datetime_format=True,
11 cache_dates=True)1# WRITE
2df.to_csv(path, index=False) # don't incldue index1# FROM A LIST
2pd.DataFrame(a_list, colummns=['col_name'])1# FROM A DICTIONARY
2names = ['John', 'Thi', 'Bi', 'Beo', 'Chang']
3ages = [10, 20, 21, 18, 11]
4marks = [8, 9, 10, 6, 8]
5city = ['Ben Tre', 'Paris', 'Ho Chi Minh Ville', 'New York', 'DC']
6
7my_dict = {'Name':names, 'Ages':ages, 'Marks':marks, 'Place': city}
8students = pd.DataFrame(my_dict)1# a column
2df['new_col] = [new_values]1# a row
2df.loc['new_index'] = [new_value]1# add a new col based on another's values
2df_im = df0.copy()[['col']]
3df_im['status'] = df0['col'].apply(lambda row: 1 if row>=80 else 0)1# shuffle all rows and reset the index
2df_new = df.sample(frac=1).reset_index(drop=True)1df.sort_values(by='col1', ascending=False)Select a single value (with condition): Get the mark of
Thi (9).1# interchange `.values[0]` and `.iloc[0]`
2df[df.Name=='Thi'].Marks.values[0]
3df.loc[df.Name=='Thi', 'Marks'].values[0]1# with indexes
2df.iloc[1,2] # row 2, column 31# column's name with row's index
2df[['Marks']].iloc[1].values[0] # column 'Marks', row 21# column's index with row's value
2df[df.Name=='Thi'].iloc[:,2].values[0] # column 3, row of 'Thi'1df.loc[1:5, 'col']Select a column (returns a
Series)1# with column's name
2df['Name']
3df.loc[:, 'Name']1# with an index
2df.iloc[:,0]Returns a
pd.DataFrame1df[['Name']]
2df.loc[:, ['Name']]1# with an index
2df.iloc[:,[0]]Select multi-columns (type
DataFrame): Get columns Name & Place:1# using columns's names
2df[['Name', 'Place']]
3df.loc[:, ['Name', 'Place']]1# using indexes
2df.iloc[:, [0,-1]]Select a row (returns a
Series)1# with an index
2df.iloc[1]1# with a condition
2df[df['Name']=='Thi'] # DataFrame
3df[df['Name']=='Thi'].iloc[0] # Series1df[df.Name=='Thi'] # DataFrame
2df[df.Name=='Thi'].iloc[0] # Series
3df[df.Name=='Thi'].values[0] # ndarraySelect multi-rows (type
DataFrame)1# using indexes
2df.iloc[:3]
3df.loc[:2]1# with conditions
2df[df['A'].isin([3, 6])]1arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo'], ['one', 'two', 'one', 'two', 'one', 'two']]
2index = pd.MultiIndex.from_arrays(arrays)
3df = pd.DataFrame(np.random.randn(3, 6), index=['A', 'B', 'C'], columns=index)Selection,
1df.loc['A', ('baz', 'two')]10.4871851df.loc[:,('baz', 'two')]1A 0.487185
2B -0.563732
3C 0.755455
4Name: (baz, two), dtype: float64If there are some column with single name,
1arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo'], [i for i in range(2)]*3]
2index = pd.MultiIndex.from_arrays(arrays)
3df1 = pd.DataFrame(np.random.randn(3, 6), index=['A', 'B', 'C'], columns=index)1# GOOD PRACTICE
2df1['time'] = [1,2,3]
3df_rs2 = df1Selection,
1# FOR GOOD PRACTICE
2df_rs2.loc['A', ('baz', 1)]
3df_rs2.loc['A', 'baz']10.943795
20 1.482855
31 0.9437951# BAD PRACTICE
2df2 = pd.DataFrame([1,2,3], index=['A', 'B', 'C'], columns=['time'])
3df_rs1 = pd.concat([df1, df2], axis=1)Selection,
1# FOR BAD PRACTICE
2df.loc['A', [('baz', 0)]]
3df_rs1.loc['A', [('baz', i) for i in [0,1]]]1(baz, 0) 0.729023
2(baz, 0) 1.482855
3(baz, 1) 0.9437951# all columns' name at the level 1
2df.columns.set_levels(['b1','c1','f1'], level=1, inplace=True)1df.columns = df.columns.droplevel()1 a
2 b c b c
30 1 2 -> 0 1 2
41 3 4 1 3 41df1.equals(df2)1# Invert True/False value in Series
2s = pd.Series([True, True, False, True])
3~s1# Convert True / False to 1 / 0
2df['col'] = df['col'].astype(int)
3# int or float