python - removing rows with any column containing NaN, NaTs, and nans -

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currently have data below:

df_all.head() out[2]:     unnamed: 0 symbol       date      close       weight 0        4061      2016-01-13  36.515889   (0.000002) 1        4062     aa 2016-01-14  36.351784    0.000112  2        4063    aac 2016-01-15  36.351784   (0.000004) 3        4064    aal 2016-01-19  36.590483    0.000006  4        4065   aamc 2016-01-20  35.934062    0.000002   df_all.tail() out[3]:           unnamed: 0 symbol date  close weight 1252498    26950320    nan  nat   9.84    nan 1252499    26950321    nan  nat  10.26    nan 1252500    26950322    nan  nat   9.99    nan 1252501    26950323    nan  nat   9.11    nan 1252502    26950324    nan  nat   9.18    nan  df_all.dtypes out[4]:  unnamed: 0             int64 symbol                object date          datetime64[ns] close                float64 weight                object dtype: object

as can seen, getting values in symbol of nan, nat date , nan weight.

my goal: want remove row has column containing nan, nat or nan , have new df_clean result

i don't seem able apply appropriate filter? not sure if have convert datatypes first (although tried well)

since, symbol 'nan' not caught dropna() or isnull(). need cast symbol'nan' np.nan

try this:

 df["symbol"] = np.where(df["symbol"]=='nan',np.nan, df["symbol"] )  df.dropna()

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