Python：よく使うコードまとめ

import pandas as pd

def b4_day_close( df ) :
  b4_date, b4_close, day_close = None, None, None
  def apply_logic( row ):
    nonlocal b4_date, b4_close, day_close
    today, hour = row.name, row.name.hour
    diff_seconds = ( today - b4_date ).seconds if b4_date else None
    if diff_seconds and diff_seconds > 60 and hour == 16 :
      day_close = b4_close
    b4_date, b4_close = today, row["c"]
    return day_close
  return apply_logic

df['b4_day_c'] = df.apply( b4_day_close( df ), axis=1 )

import pandas as pd

dt={ "x": [ 1100, 1200, 1300 ],
     "y": [ 2100, 2200, 2300 ], }
df = pd.DataFrame(dt)

def fnc( df ) :
    return df["x"]+1,df["x"]+2

df[["a","b"]] = df.apply( fnc( df ), axis=1, result_type='expand' )

import pandas as pd
symbol = 7203
df.loc[ 
  ( df['銘柄コード'] == symbol ) & ( df['項目'] == '修正1株益 前年同期比' ), 
  [ '年月', '数値' ] 
].sort_values( [ '年月' ], ascending=[ True ] )

import pandas as pd

df_l = _get_df( "N225f", year )
df_r = _get_df( "Topixf", year )
df = df_l.join( df_r, lsuffix="_n", rsuffix="_t" )

df_append = pd.DataFrame( {
  'item1' : [ item1 ],
  'item2' : [ item2 ],
  'item3' : [ item3 ],
} )
df_target = pd.concat( [ df_target, df_append ], axis=0 ) 

df.to_pickle( BASE_DIR + file_name )

df = pd.read_pickle( BASE_DIR + file_name )

from pandas_datareader import data as pdr
pdr.get_data_yahoo("^GSPC","1980-1-1").dropna()

from pandas_datareader import data as pdr
pdr.get_data_fred( 'USSLIND', '1980-1-1' ).dropna()

!pip install pandas_ta

def calc_stg( s, m, l ) :
  return np.array( list( map(
    lambda s, m, l :
    1 if s > m and m > l else \
    2 if m > s and s > l else \
    3 if m > l and l > s else \
    4 if l > m and m > s else \
    5 if l > s and s > m else \
    6 if s > l and l > m else \
    np.nan, s, m, l ) ) )

def calc_stg_span( stg ) :
  result = []
  for i in range( len( stg ) ) :
    if i == 0 :
      result.append( np.nan )
    else :
      s   = stg[ i ]
      b4s = stg[ i - 1 ]
      result.append(
        0 if ( b4s != 1 and b4s != 2 ) and ( s == 1 or s == 2 ) else\
        0 if ( b4s != 4 and b4s != 5 ) and ( s == 4 or s == 5 ) else\
        0 if ( b4s == 1 or b4s == 2 ) and ( s != 1 and s != 2 ) else\
        0 if ( b4s == 4 or b4s == 5 ) and ( s != 4 and s != 5 ) else\
        result[ i - 1 ] + 1 if s == 1 or s == 2 or s == 4 or s == 5 else\
        result[ i - 1 ] - 1 )
  return np.array( result )

import pandas as pd
import datetime as dt

def _get_df( symbol, year ) :
  df = pd.read_excel( f'{BASEDIR}/{symbol}_{year}.xlsx',
                     names=("date","time","open","high","low","close","volume"),
                     sheet_name='1min' )
  df["time"] = df["time"].astype( str )
  df["Datetime"] = pd.to_datetime( df["date"].dt.strftime("%x ") + df["time"] )
  # Datetime：取引日（営業日） → カレンダーの調整
  df.loc[ df["time"]>="16:30:00", "Datetime" ] = df.loc[ df["time"]>="16:30:00", "Datetime" ] + dt.timedelta( days=-1 )
  df = df.set_index("Datetime")

import datetime as dt
sample_datetime + dt.timedelta( days=-1 )

import pandas as pd
diff_seconds = ( this_datetime - b4_datetime ).seconds