Python Pandas usage experience

Function Prototypes:
Https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html#pandas.DataFrame.fillna

Pad/ffill: Fills the missing value with the previous non-missing value
Backfill/bfill: Fills the missing value with the next non-missing value
None: Specify a value to replace the missing value

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# Coding:utf-8 Import PandasAs PD DF = PD. DataFrame ([[1,None,2], [None,3,None], [None,4,5]) Print' Origin ') Print (DF) # 0 1 2 # 0 1.0 NaN 2.0 # 1 Nan 3.0 nan # 2 NaN 4.0 5.0 Print' Left ') data = Df.bfill (axis=1). iloc[:,0] Print (data) # 0 1.0 # 1 3.0 # 2 4.0 Print' Up ') data = Df.bfill (). iloc[:,-1] Print (data) # 0 2.0 # 1 5.0 # 2 5.0 Print' Left ') data = Df.fillna (method=' Bfill ', axis=1) Print (data) # 0 1 2 # 0 1.0 2.0 2.0 # 1 3.0 3.0 NaN # 2 4.0 4.0 5.0 Print' Up ') data = Df.fillna (method=' Bfill ') Print (data) # 0 1 2 # 0 1.0 3.0 2.0 # 1 NaN 3.0 5.0 # 2 NaN 4.0 5.0 Print' Right ') data = Df.fillna (method=' Ffill ', axis=1) Print (data) # 0 1 2 # 0 1.0 1.0 2.0 # 1 NaN 3.0 3.0 # 2 NaN 4.0 5.0 Print' Down ') data = Df.fillna (method=' Ffill ') Print (data) # 0 1 2 # 0 1.0 NaN 2.0 # 1 1.0 3.0 2.0 # 2 1.0 4.0 5.0 Print' Left ') data = Df.fillna (method=' Backfill ', axis=1) Print (data) # 0 1 2 # 0 1.0 2.0 2.0 # 1 3.0 3.0 NaN # 2 4.0 4.0 5.0 Print' Up ') data = Df.fillna (method=' Backfill ') Print (data) # 0 1 2 # 0 1.0 3.0 2.0 # 1 NaN 3.0 5.0 # 2 NaN 4.0 5.0 Print' Right ') data = Df.fillna (method=' pad ', axis=1) Print (data) # 0 1 2 # 0 1.0 1.0 2.0 # 1 NaN 3.0 3.0 # 2 NaN 4.0 5.0 Print (' down ') data = Df.fillna (method=' pad ') Print (data) # 0 1 2 # 0 1.0 NaN 2.0 # 1 1.0 3.0 2.0 # 2 1.0 4.0 5.0

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Import Pandas as PD Import NumPy as NP DF = PD. DataFrame ([[Np.nan, 2, Np.nan, 0], [3, 4, Np.nan, 1], [Np.nan, Np.nan, Np.nan, 5], [Np.nan, 3, Np.nan, 4]], columns = List (' ABCD ')) Print (DF) # A B C D # 0 Nan 2.0 nan 0 # 1 3.0 4.0 NaN 1 # 2 Nan Nan nan 5 # 3 Nan 3.0 nan 4 Print (Df.fillna (0)) # A B C D # 0 0.0 2.0 0.0 0 # 1 3.0 4.0 0.0 1 # 2 0.0 0.0 0.0 5 # 3 0.0 3.0 0.0 4 print (Df.fillna (method= ' Ffill ')) # A B C D # 0 NaN 2.0 Nan 0 # 1 3.0 4.0 NaN 1 # 2 3.0 4.0 NaN 5 # 3 3.0 3.0 Na N 4 values = {' A ': 0, ' B ': 1, ' C ': 2, ' D ': 3} print ( Df.fillna (value=values)) # A B C D # 0 0.0 2.0 2.0 0 # 1 3.0 4.0 2.0 1 # 2 0.0 1.0 2.0 5 # 3 0.0 3.0 2.0 4 print (Df.fillna (value=values, limit=1)) # A B C D # 0 0.0 2.0 2.0 0 # 1 3.0 4.0 nan 1 # 2 nan 1.0 nan 5 # 3 Nan 3.0 nan 4

If the imported dataframe contains a dictionary, use data.join (data[' A10 '].apply (json.loads). Apply (PD. Series ) to split the dictionaries into different columns.

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Import Pandas as PD Import JSON filename = ' Top5.txt ' data = pd.read_csv (filename, sep= "\ T", Header=none) # test model.8.10 modelname 810 8101 2018-03-28 04:21:13 2018-03-28 04:21:13 # 1 0 2018-04-02 14:50:54 {"Cell_info": "LTE plmn:46000 earfcn:38400 (B39) cell Identity #: 197539969 pci:141 tac:37884 rssi:-65 rsrp:-95 rsrq:-11 sinr*10:133 EMM state:registered # service State:normal reg DOMAIN:CS_PS Lte_tx_power tx = 9 Lte_rx_chain0 rssi=-64 rsrp=-94 # sinr=133 lte_rx_chain1 rssi=-69 rsrp=-99 sinr=118 "," Log_from ":" Com.android.phone ", # "Reg_at_time": "31112", "rat": "+", "Reg_during_time": "3554", "HPLMN": "46002"} 2018-04-02 columns = [] For I in range (Data.shape[1]): Columns.Append (' A ' + str (i)) Data.columns = Columns Print (Data.columns) # Index ([' A0 ', ' A1 ', ' A2 ', ' A3 ', ' A4 ', ' A5 ', ' A6 ', ' A7 ', ' A8 ', ' A9 ', ' A10 ', ' A11 '), # dtype= ' object ') Print (data[' A10 ') # 0 {"Cell_info": "LTE plmn:46000 earfcn:38400 (B39 ... data = Data.join (data[' A10 '].apply (json.loads). Apply (PD. Series)) Print (Data.columns) # Index ([' A0 ', ' A1 ', ' A2 ', ' A3 ', ' A4 ', ' A5 ', ' A6 ', ' A7 ', ' A8 ', ' A9 ', ' A10 ', # ' A11 ', ' cell_info ', ' hplmn ', ' log_from ', ' rat ', ' reg_at_time ', # ' Reg_during_time '), # dtype= ' object ')

Python Pandas usage experience